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3175 | 1 | 3177 | null | 2 | 3402 | I have a data which is 100x1 vector. How can I display its empirical pdf in Matlab? Also, if I want to compare the pdf of three vectors on the same graph, then how to do that?
Right now I am using [pdfplot.m](http://www.mathworks.com/matlabcentral/fileexchange/8578-pdfplot) file to plot my empirical pdf, however when I... | How can I display empirical pdf of my 100x1 vector data in Matlab? | CC BY-SA 2.5 | null | 2010-09-29T19:12:08.840 | 2010-09-30T07:32:21.337 | null | null | null | [
"distributions",
"multiple-comparisons",
"matlab",
"density-function",
"data-visualization"
] |
3176 | 1 | null | null | 1 | 2473 | Imagine 2 models: 1 for production, 1 for revenue, have cross-section and time dummies.
Is it permissable to use CS in one and time in the other? How do I justify this? F-test for fixed effects is indicating this and also dummies I want to omit in each case are not really significant.
| Cross-section and fixed effects models | CC BY-SA 2.5 | null | 2010-09-29T20:47:57.117 | 2011-03-06T20:39:36.190 | 2010-09-29T23:31:12.340 | null | null | [
"regression",
"cross-section"
] |
3177 | 2 | null | 3175 | 1 | null | the problem is that `histc` plots bars instead of, say lines; the bars are plotted over one another. you should edit `pdfplot` to plot lines instead. look for the `hist` command and alter it.
| null | CC BY-SA 2.5 | null | 2010-09-29T21:50:43.303 | 2010-09-29T21:50:43.303 | null | null | 795 | null |
3178 | 2 | null | 1455 | 1 | null | Since the odds ratio cannot be negative, it is restricted at the lower end, but not at the upper end, and so has a skew distribution.
| null | CC BY-SA 2.5 | null | 2010-09-30T03:05:13.367 | 2010-09-30T03:05:13.367 | null | null | null | null |
3179 | 1 | 3217 | null | 15 | 2673 | I'm working on a project that involves 14 variables and 345,000 observations for housing data (things like year built, square footage, price sold, county of residence, etc). I'm concerned with trying to find good graphical techniques and R libraries that contain nice plotting techniques.
I'm already seeing what in ggp... | A good way to show lots of data graphically | CC BY-SA 2.5 | null | 2010-09-30T04:37:50.027 | 2017-07-04T18:55:07.973 | 2010-10-08T16:06:40.877 | 8 | 1118 | [
"r",
"data-visualization",
"large-data",
"exploratory-data-analysis"
] |
3180 | 1 | null | null | 7 | 277 | I was recently consulting a researcher in the following situation.
Context:
- data were collected over four years at around 50 participants per year (participants had a specific diagnosed clinical psychology disorder and were difficult to obtain in large numbers); participants were only measured once (i.e., it's not ... | Dealing with missing data due to variable not being measured over initial period of a study | CC BY-SA 2.5 | null | 2010-09-30T04:52:04.513 | 2010-10-02T19:05:07.083 | 2010-10-01T06:57:58.187 | 930 | 183 | [
"scales",
"missing-data",
"data-imputation"
] |
3181 | 1 | 3184 | null | 28 | 1864 | If you think back, to when you first started with time series analysis. What tools, R packages and internet resources do you wish you had known about?
What I'm trying to ask is, where should one start? Specifically, are there any resources for R that really boil it down for one who is "new" to time series analysis with... | Getting seRious about time series with R | CC BY-SA 2.5 | null | 2010-09-30T05:08:38.800 | 2014-02-25T05:57:19.613 | 2010-09-30T11:05:59.217 | 776 | 776 | [
"r",
"time-series"
] |
3182 | 2 | null | 3179 | 6 | null | I'd recommend taking a look at [GGobi](http://www.ggobi.org/), which also has an R interface, at least for exploratory purposes. It has a number of graphical displays especially useful for dealing with a large number observations and variables and for linking these together. You might want to start by watching some o... | null | CC BY-SA 2.5 | null | 2010-09-30T05:09:55.430 | 2010-09-30T20:05:28.320 | 2010-09-30T20:05:28.320 | 251 | 251 | null |
3183 | 2 | null | 3179 | 3 | null | [Mondrian](http://rosuda.org/Mondrian/Mondrian.html) provides interactive features and handles quite large data sets (it's in Java, though).
[Paraview](http://www.paraview.org/) includes 2D/3D viz. features.
| null | CC BY-SA 2.5 | null | 2010-09-30T06:44:28.390 | 2010-09-30T06:44:28.390 | null | null | 930 | null |
3184 | 2 | null | 3181 | 26 | null | There is a [Time Series Task View](http://cran.r-project.org/web/views/TimeSeries.html) that aims to summarize all the time series packages for R. It highlights some core packages that provide some essential functionality.
I would also recommend the book by [Shumway and Stoffer](http://www.stat.pitt.edu/stoffer/tsa2/) ... | null | CC BY-SA 3.0 | null | 2010-09-30T06:55:51.197 | 2014-02-25T05:57:19.613 | 2014-02-25T05:57:19.613 | 159 | 159 | null |
3185 | 2 | null | 3181 | 13 | null | I've found the UseR! series book [Introductory Time Series with R](http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-88697-8) by Cowpertwait and Metcalfe very useful in translating my time series statistics textbooks into R-speak.
| null | CC BY-SA 3.0 | null | 2010-09-30T07:02:54.690 | 2013-06-27T13:17:33.023 | 2013-06-27T13:17:33.023 | 22047 | 1390 | null |
3186 | 2 | null | 3175 | 2 | null | I would suggest using the [ksdensity](http://www.mathworks.com/help/toolbox/stats/ksdensity.html) function. In the following example I compare the pdf of the data in column 1 of matrix 1 with the pdf of the data in column 2 of matrix 2:
```
[f,x] = ksdensity(mat1(:,1));
plot(x,f,'--b');hold
[f,x] = ksdensity(mat2(:,2))... | null | CC BY-SA 2.5 | null | 2010-09-30T07:24:51.460 | 2010-09-30T07:32:21.337 | 2010-09-30T07:32:21.337 | 339 | 339 | null |
3187 | 2 | null | 3181 | 6 | null | For ecologists, [Tree diversity analysis](http://www.worldagroforestry.org/units/library/books/PDFs/Kindt%20b2005.pdf) can be a first healthy step into the right direction. The book is free, it comes with an R package ([BiodiversityR](http://cran.r-project.org/web/packages/BiodiversityR/index.html)) and gives you a tas... | null | CC BY-SA 2.5 | null | 2010-09-30T07:36:56.130 | 2011-04-01T04:38:17.623 | 2011-04-01T04:38:17.623 | 1381 | 144 | null |
3188 | 2 | null | 3179 | 6 | null | I feel you are actually asking two questions: 1) what types of visualizations to use and 2) what R package can produce them.
In the case of what type of graph to use, there are many, and it depends on your needs (e.g: types of variables - numeric, factor, geographic etc, and the type of connections you are interested ... | null | CC BY-SA 3.0 | null | 2010-09-30T08:08:11.903 | 2017-07-04T18:55:07.973 | 2017-07-04T18:55:07.973 | 11887 | 253 | null |
3189 | 5 | null | null | 0 | null | Usually, forecasting is applied to time series data where future values of a series are predicted based on past observations, possibly leveraging predictors. It is contrasted with [prediction](/questions/tagged/prediction) in Cressie & Wikle Statistics for Spatio-Temporal Data, p. 17:
>
Uncertainty in data, processes ... | null | CC BY-SA 4.0 | null | 2010-09-30T08:09:08.867 | 2022-11-23T09:40:48.703 | 2022-11-23T09:40:48.703 | 1352 | 159 | null |
3190 | 4 | null | null | 0 | null | Prediction of the future events. It is a special case of [prediction], in the context of [time-series]. | null | CC BY-SA 3.0 | null | 2010-09-30T08:09:08.867 | 2017-05-24T14:47:09.810 | 2017-05-24T14:47:09.810 | 28666 | 159 | null |
3191 | 2 | null | 2910 | 81 | null | I am compiling a quick series of guidelines I found on [SO](http://www.stackoverflow.com) (as suggested by @Shane), [Biostar](http://biostar.stackexchange.com/) (hereafter, BS), and this SE. I tried my best to acknowledge ownership for each item, and to select first or highly upvoted answer. I also added things of my o... | null | CC BY-SA 3.0 | null | 2010-09-30T10:44:48.180 | 2014-04-09T09:40:19.067 | 2017-05-23T12:39:26.203 | -1 | 930 | null |
3192 | 2 | null | 3156 | 1 | null | Reporting a CI around a mean is not reporting the distribution of values, only an estimate of how well you captured that mean value. It will always get smaller as n goes up. It's NOT what you want because you want to see how well a point fits into a distribution.
With your fairly large N's you might be able to do a n... | null | CC BY-SA 2.5 | null | 2010-09-30T10:58:23.187 | 2010-09-30T10:58:23.187 | null | null | 601 | null |
3193 | 1 | 3205 | null | 7 | 235 | Hello data analyst community. I have the following problem:
Given a set of n units and a timeline in days. A unit may be active at a certain day to a certain degree (in range from 0.0 to 1.0). A desirable outcome is that if a unit is active, it should be active for a series of consecutive days (or at maximum with one d... | Visualizing activity frequency | CC BY-SA 2.5 | null | 2010-09-30T11:16:25.453 | 2010-10-01T11:59:49.043 | 2010-10-01T11:59:49.043 | 264 | 264 | [
"time-series",
"data-visualization"
] |
3194 | 1 | 3198 | null | 29 | 8963 | How can I test the fairness of a twenty sided die (d20)? Obviously I would be comparing the distribution of values against a uniform distribution. I vaguely remember using a Chi-square test in college. How can I apply this to see if a die is fair?
| How can I test the fairness of a d20? | CC BY-SA 2.5 | null | 2010-09-30T13:04:31.537 | 2018-05-28T23:55:32.270 | 2013-11-04T02:42:31.273 | 805 | 1456 | [
"hypothesis-testing",
"chi-squared-test",
"goodness-of-fit",
"uniform-distribution",
"dice"
] |
3195 | 2 | null | 3171 | 1 | null | You would normally make the assumption of independence of observations in your modelling.
Alternatively if you expected correlation between observations it would be good to model this and estimate that correlation. You can't do this as you don't know which observations are likely to be correlated.
If you assume ind... | null | CC BY-SA 3.0 | null | 2010-09-30T13:14:28.583 | 2015-12-02T03:14:19.143 | 2015-12-02T03:14:19.143 | 22228 | 521 | null |
3196 | 2 | null | 3194 | 10 | null | Do you want to do it by hand, or in excel ?
If you want to do it in [R](http://www.r-project.org/), you can do it this way:
Step 1: roll your die (let's say) 100 times.
Step 2: count how many times you got each of your numbers
Step 3: put them in R like this (write the number of times each die roll you got, instead of ... | null | CC BY-SA 2.5 | null | 2010-09-30T13:34:56.983 | 2010-09-30T14:28:19.940 | 2010-09-30T14:28:19.940 | 253 | 253 | null |
3197 | 2 | null | 3194 | 7 | null | If you are interested in just checking the number of times each number appears, then a Chi-squared test would be suitable. Suppose you roll a die N times. You would expect each value to come up N/20 times. All a chi-square test does is compare what you observed with what you get. If this difference is too large, then t... | null | CC BY-SA 2.5 | null | 2010-09-30T13:40:00.953 | 2010-09-30T14:05:29.147 | 2017-04-13T12:44:26.710 | -1 | 8 | null |
3198 | 2 | null | 3194 | 14 | null | Here's an example with R code. The output is preceded by #'s.
A fair die:
```
rolls <- sample(1:20, 200, replace = T)
table(rolls)
#rolls
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# 7 8 11 9 12 14 9 14 11 7 11 10 13 8 8 5 13 9 10 11
chisq.test(table(rolls), p = rep(0.05, 20))
# Ch... | null | CC BY-SA 2.5 | null | 2010-09-30T13:44:01.660 | 2010-09-30T13:51:53.710 | 2010-09-30T13:51:53.710 | 521 | 521 | null |
3199 | 1 | 3210 | null | 8 | 1861 | I have a 2 dim matrix, and I want to know e.g. all the higher values are in the upper left corner. I can't just project it into R^3 and use a standard clustering algorithm because I don't want to consider the value as a dimension by itself.
Is there an algorithm I can use for this?
EDIT:
To reformulate it, suppose it ... | Clustering of a matrix (homogeneity measurement) | CC BY-SA 2.5 | null | 2010-09-30T13:58:08.637 | 2019-08-02T14:17:24.650 | 2019-08-02T14:17:24.650 | 919 | 900 | [
"clustering",
"spatial"
] |
3200 | 1 | 3202 | null | 61 | 48975 | Lets assume you are a social science researcher/econometrician trying to find relevant predictors of demand for a service. You have 2 outcome/dependent variables describing the demand (using the service yes/no, and the number of occasions). You have 10 predictor/independent variables that could theoretically explain th... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | CC BY-SA 3.0 | null | 2010-09-30T14:07:56.490 | 2016-11-03T21:24:08.560 | 2012-06-05T19:10:37.397 | 7290 | 1458 | [
"regression",
"multivariate-analysis",
"predictive-models",
"multiple-regression",
"multiple-comparisons"
] |
3201 | 1 | 3218 | null | 11 | 852 | I have a database containing a large number of experts in a field. For each of those experts i have a variety of attributes/data points like:
- number of years of experience.
- licenses
- num of reviews
- textual content of those reviews
- The 5 star rating on each of those reviews, for a number of factors like sp... | How do I order or rank a set of experts? | CC BY-SA 2.5 | null | 2010-09-30T14:14:06.623 | 2013-06-28T13:25:14.263 | 2017-04-13T12:44:36.927 | -1 | 1459 | [
"rating",
"valuation"
] |
3202 | 2 | null | 3200 | 53 | null | It seems your question more generally addresses the problem of identifying good predictors. In this case, you should consider using some kind of penalized regression (methods dealing with variable or [feature selection](http://www.google.fr/url?sa=t&source=web&cd=1&ved=0CBgQFjAA&url=http%3A%2F%2Fen.wikipedia.org%2Fwiki... | null | CC BY-SA 3.0 | null | 2010-09-30T14:33:11.650 | 2015-10-28T12:48:38.413 | 2017-04-13T12:44:41.967 | -1 | 930 | null |
3203 | 2 | null | 3199 | 2 | null | The goal is just to find out a measure that will tell us how mixed up all the pixels are. Given 2 matrices of data with the exact same distribution of values, if the first one's values are ordered or clumped together in spatial groups and the 2nd one's values are well-dispersed (high points and not near other high poin... | null | CC BY-SA 2.5 | null | 2010-09-30T14:35:49.713 | 2010-09-30T14:35:49.713 | null | null | null | null |
3204 | 2 | null | 3193 | 1 | null | How about creating small timelines for each unit, one on top of the other, sorted in order of most to least active? Think [sparklines](http://en.wikipedia.org/wiki/Sparkline)
You could probably do something like highlight the inactive time as either a shaded portion of the chart, or a colored portion of the unit's tim... | null | CC BY-SA 2.5 | null | 2010-09-30T15:03:59.807 | 2010-09-30T15:03:59.807 | null | null | 298 | null |
3205 | 2 | null | 3193 | 5 | null | You might be trying to incorporate too much information into the graphic. The essence of the visualization seems to be the frequency with which units are active more than one day and, possibly, the times at which those units are active.
Just to generate ideas--because there are many possible solutions--consider a disp... | null | CC BY-SA 2.5 | null | 2010-09-30T15:36:57.600 | 2010-09-30T15:36:57.600 | null | null | 919 | null |
3206 | 2 | null | 3200 | 26 | null | To a great degree you can do whatever you like provided you hold out enough data at random to test whatever model you come up with based on the retained data. A 50% split can be a good idea. Yes, you lose some ability to detect relationships, but what you gain is enormous; namely, the ability to replicate your work b... | null | CC BY-SA 2.5 | null | 2010-09-30T15:53:06.523 | 2010-09-30T15:53:06.523 | null | null | 919 | null |
3207 | 1 | 3256 | null | 4 | 323 | I previously asked this [question](https://stats.stackexchange.com/q/2917/1381) about the validity of my solutions for for $SE$ given $n$, $\bar{X_i}$ and summary statistics from post-hoc multiple comparisons such as Fisher's $LSD$ and Tukey's $HSD$, but I would like a general approach that can be applied to other post... | Given sample size, group means, and misc. post-hoc range statistics, can you suggest a good way to estimate variance through simulation? | CC BY-SA 2.5 | null | 2010-09-30T16:02:39.110 | 2010-10-02T00:22:45.550 | 2017-04-13T12:44:24.667 | -1 | 1381 | [
"r",
"standard-deviation",
"variance",
"meta-analysis"
] |
3208 | 2 | null | 3200 | 0 | null | You can do a seemingly unrelated regression and use an F test. Put your data in a form like this:
```
Out1 1 P11 P12 0 0 0
Out2 0 0 0 1 P21 P22
```
so that the predictors for your first outcome have their values when that outcome is the y variable and 0 otherwise and vice-versa. So your y is a list of both out... | null | CC BY-SA 2.5 | null | 2010-09-30T16:04:05.910 | 2010-09-30T16:04:05.910 | null | null | 401 | null |
3209 | 2 | null | 3201 | 0 | null | Do you think that you could quantify all those attributes?
If yes, I would suggest performing a principal component analysis. In the general case where all the correlations are positive (and if they aren't, you can easily get there using some transformation), the first principal component can be considered as a measur... | null | CC BY-SA 2.5 | null | 2010-09-30T16:06:50.210 | 2010-09-30T16:06:50.210 | null | null | 339 | null |
3210 | 2 | null | 3199 | 6 | null | This question is about spatial correlation. Many methods exist to characterize and quantify this. What they all have in common is comparing values at one location to those at nearby locations. Usually, the reference distribution is some kind of spatial stochastic process where data are generated independently from p... | null | CC BY-SA 2.5 | null | 2010-09-30T16:10:52.277 | 2010-09-30T16:10:52.277 | null | null | 919 | null |
3211 | 2 | null | 3201 | 3 | null | Ultimately this may not be solely a statistical exercise. PCA is a very powerful quantitative method that will allow you to generate a score or weights on its first few principal components that you can use for ranking. However, explaining what the principal components are is very challenging. They are quantitative ... | null | CC BY-SA 2.5 | null | 2010-09-30T16:15:40.633 | 2010-09-30T18:39:41.760 | 2010-09-30T18:39:41.760 | 1329 | 1329 | null |
3212 | 1 | 3213 | null | 38 | 60744 | I was wondering what are the differences between Mode, Class and Type of R objects?
Type of a R object can be obtained by typeof() function, mode by mode(), and class by class().
Also any other similar functions and concepts that I missed?
Thanks and regards!
| Mode, Class and Type of R objects | CC BY-SA 2.5 | null | 2010-09-30T16:19:47.023 | 2017-02-01T17:42:22.487 | 2010-09-30T16:25:25.557 | 1005 | 1005 | [
"r"
] |
3213 | 2 | null | 3212 | 20 | null | The `class()` is used to define/identify what "type" an object is from the point of view of object-oriented programming in R. So for
```
> x <- 1:3
> class(x)
[1] "integer"
```
any generic function that has an "integer" method will be used.
`typeof()` gives the "type" of object from R's point of view, whilst `mode()` ... | null | CC BY-SA 2.5 | null | 2010-09-30T16:32:06.770 | 2010-09-30T16:32:06.770 | null | null | 1390 | null |
3214 | 1 | null | null | 4 | 269 | In my animal experiments, I do survival studies, which generate Kaplan-Meier survival curves for each group, which I then compare with an appropriate log rank test.
My question is: If I have run a survival experiment with identical variables, say, five times, and the final outcome (in very layman's terms) happens to be... | Inter-experimental variation in survival experiment - how to estimate variability? | CC BY-SA 2.5 | null | 2010-09-30T18:19:59.433 | 2010-10-01T01:56:17.783 | 2010-09-30T18:40:21.410 | 930 | 1464 | [
"estimation",
"survival",
"variability"
] |
3215 | 1 | 3216 | null | 15 | 3763 | Addition, subtraction, multiplication and division of normal random variables are well defined, but what about trigonometric operations?
For instance, let us suppose that I'm trying to find the angle of a triangular wedge (modelled as a right-angle triangle) with the two catheti having dimensions $d_1$ and $d_2$, both... | Trigonometric operations on standard deviations | CC BY-SA 3.0 | null | 2010-09-30T19:21:21.627 | 2022-08-06T19:45:51.410 | 2017-06-06T01:00:33.783 | 11887 | 77 | [
"distributions",
"normal-distribution",
"circular-statistics",
"saddlepoint-approximation"
] |
3216 | 2 | null | 3215 | 17 | null | In this interpretation, the triangle is a right triangle of side lengths $X$ and $Y$ distributed binormally with expectations $\mu_x$ and $\mu_y$, standard deviations $\sigma_x$ and $\sigma_y$, and correlation $\rho$. We seek the distribution of $\arctan(Y/X)$. To this end, standardize $X$ and $Y$ so that
$$X = \sigm... | null | CC BY-SA 4.0 | null | 2010-09-30T20:19:47.273 | 2022-08-06T19:45:51.410 | 2022-08-06T19:45:51.410 | 79696 | 919 | null |
3217 | 2 | null | 3179 | 13 | null | The best "graph" is so obvious nobody has mentioned it yet: make maps. Housing data depend fundamentally on spatial location (according to the old saw about real estate), so the very first thing to be done is to make a clear detailed map of each variable. To do this well with a third of a million points really requir... | null | CC BY-SA 2.5 | null | 2010-09-30T20:40:28.717 | 2010-09-30T20:40:28.717 | null | null | 919 | null |
3218 | 2 | null | 3201 | 12 | null | People have invented numerous systems for rating things (like experts) on multiple criteria: visit the Wikipedia page on [Multi-criteria decision analysis](http://en.wikipedia.org/wiki/Multi-criteria_decision_analysis) for a list. Not well represented there, though, is one of the most defensible methods out there: [Mu... | null | CC BY-SA 2.5 | null | 2010-09-30T21:01:26.463 | 2010-09-30T21:01:26.463 | null | null | 919 | null |
3219 | 2 | null | 3158 | 8 | null | I found that the dependency graph in Flare is also similar to what I want:
[http://flare.prefuse.org/apps/dependency_graph](http://flare.prefuse.org/apps/dependency_graph)
| null | CC BY-SA 2.5 | null | 2010-09-30T22:37:31.757 | 2010-09-30T22:37:31.757 | null | null | 1106 | null |
3220 | 2 | null | 3215 | 14 | null | You are looking at circular statistics and in particular a circular distribution called the projected normal distribution.
For some reason this topic can be a little hard to google, but the two major texts on circular statistics are [The Statistical Analysis of Circular Data](http://books.google.com.au/books?id=wGPj3Eo... | null | CC BY-SA 3.0 | null | 2010-09-30T22:55:48.303 | 2014-08-26T13:48:20.407 | 2014-08-26T13:48:20.407 | 919 | 352 | null |
3221 | 1 | 3224 | null | 4 | 166 | I was wondering how to create a vector with equivalent spacing between its consecutive elements in R? In Matlab, I can do [start:step:end].
Also if I want to plot a function with analytical form, do I have to evaluate the function on some sample points in its domain and plot these pair of points? Is there a R function ... | How to create a vector with equivalent spacing between its consecutive elements in R? | CC BY-SA 3.0 | null | 2010-10-01T00:16:07.093 | 2014-07-02T15:23:55.260 | 2014-07-02T15:23:55.260 | 196 | 1005 | [
"r",
"data-visualization"
] |
3222 | 2 | null | 3221 | 3 | null | See [seq](http://stuff.mit.edu/afs/sipb/project/r-project/arch/i386_rhel3/lib/R/library/base/html/seq.html) for sequence generation:
```
seq(from, to, by)
```
or `?seq` for help.
| null | CC BY-SA 2.5 | null | 2010-10-01T00:20:28.803 | 2010-10-01T00:20:28.803 | null | null | 251 | null |
3223 | 2 | null | 3158 | 7 | null | I would just add:
As you point out, Flare has the dependency graph, which Aleks Jakulin [argued was similar but better](http://www.stat.columbia.edu/~cook/movabletype/archives/2009/06/visualizing_tab.html). This was based originally on the ["Hierarchical Edge Bundles:
Visualization of Adjacency Relations in Hierarchic... | null | CC BY-SA 3.0 | null | 2010-10-01T00:22:16.480 | 2014-11-17T10:54:44.117 | 2014-11-17T10:54:44.117 | 22047 | 5 | null |
3224 | 2 | null | 3221 | 4 | null |
- Use seq as suggested by ars
- For example, plot(sin, -pi, 2*pi).
| null | CC BY-SA 2.5 | null | 2010-10-01T00:26:46.690 | 2010-10-01T00:26:46.690 | null | null | 159 | null |
3228 | 2 | null | 2910 | 1 | null | Just my 2 cents. I've found Notepad++ useful for this. I can maintain separate scripts (program control, data formatting, etc.) and a .pad file for each project. The .pad file call's all the scripts associated with that project.
| null | CC BY-SA 2.5 | null | 2010-10-01T00:58:04.533 | 2010-10-01T00:58:04.533 | null | null | null | null |
3229 | 2 | null | 3171 | 0 | null | You should adjust your standard errors (and p-values, confidence intervals, etc) to account for the observations not being independent. You can do this under some reasonable assumptions even though you don't know which observations are of the same person.
For example, suppose you're estimating the mean of some variabl... | null | CC BY-SA 2.5 | null | 2010-10-01T01:04:39.917 | 2010-10-01T01:04:39.917 | null | null | 1229 | null |
3230 | 2 | null | 3214 | 2 | null | I suggest you read Section 2.3 & 2.4 pp40-73 of Hosmer & Lemeshow's 1999 edition of [Applied Survival Analysis](http://rads.stackoverflow.com/amzn/click/0471154105). This gives variances of various statistics of survivorship functions, such as 1) each time (allowing confidence interval estimation), 2) mean survival, et... | null | CC BY-SA 2.5 | null | 2010-10-01T01:07:46.603 | 2010-10-01T01:56:17.783 | 2010-10-01T01:56:17.783 | 521 | 521 | null |
3231 | 2 | null | 1380 | 2 | null | Your questions is a good one (given I understand correctly). I believe you have K, 2x2 tables which correspond K different methods (call Z) and your aim is to say .. method K_1, K_2 ... K_n (K_i belongs to {1,...,K}) have some association between prediction and truth and the remaining don't have a relation. If you thin... | null | CC BY-SA 2.5 | null | 2010-10-01T01:34:16.313 | 2010-10-01T01:34:16.313 | null | null | 1307 | null |
3232 | 1 | 3234 | null | 6 | 199 | My office is going to implement a bundle of infection control measures in hospital and see if it can effectively reduce the infection rate of some pathogen. The unit of measurement will be "case per thousand patient bed days". We have chosen 4 wards for implementing the control measures for 12 months, and do the measur... | Dependent variable selection for loglinear segmented regression in time-series analysis of rare events | CC BY-SA 2.5 | null | 2010-10-01T03:25:35.153 | 2010-10-01T13:32:19.047 | 2010-10-01T13:32:19.047 | null | 588 | [
"time-series",
"epidemiology",
"monitoring"
] |
3234 | 2 | null | 3232 | 3 | null | I think you're right to conclude that there's little hope of finding a 'statistically significant' result from 4 wards over 12 months. Of course, that doesn't mean the control measures don't work — just that your sample size is far too small (and the variability too large) to have much chance of finding evidence that i... | null | CC BY-SA 2.5 | null | 2010-10-01T08:16:11.410 | 2010-10-01T08:16:11.410 | null | null | 449 | null |
3235 | 1 | 3243 | null | 37 | 19779 |
- I would like to measure the time that it takes to repeat the running of a function. Are replicate() and using for-loops equivalent? For example:
system.time(replicate(1000, f()));
system.time(for(i in 1:1000){f()});
Which is the prefered method.
- In the output of system.time(), is sys+user the actual CPU time fo... | Timing functions in R | CC BY-SA 2.5 | null | 2010-10-01T11:46:09.530 | 2010-10-02T16:06:05.113 | 2010-10-01T13:31:31.707 | 8 | 1005 | [
"r"
] |
3236 | 2 | null | 3235 | 26 | null | Regarding your two points:
- It's stylistic. I like replicate() as it is functional.
- I tend to focus on elapsed, i.e. the third number.
What I often do is
```
N <- someNumber
mean(replicate( N, system.time( f(...) )[3], trimmed=0.05) )
```
to get a trimmed mean of 90% of N repetitions of calling `f()`.
(Edited,... | null | CC BY-SA 2.5 | null | 2010-10-01T12:03:11.517 | 2010-10-01T20:35:05.737 | 2010-10-01T20:35:05.737 | 334 | 334 | null |
3237 | 2 | null | 3235 | 10 | null | You can also time with timesteps returned by `Sys.time`; this of course measures walltime, so real time computation time. Example code:
```
Sys.time()->start;
replicate(N,doMeasuredComputation());
print(Sys.time()-start);
```
| null | CC BY-SA 2.5 | null | 2010-10-01T13:30:55.063 | 2010-10-01T13:30:55.063 | null | null | null | null |
3238 | 1 | 3257 | null | 44 | 23470 | I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly.
That is to say, if the Time series were to be read into a 2D matrix, it would look something like this:
```
date T1 T2 T3 .... TN
1/1/01 100 59 42 N/A
2/1/... | Time series 'clustering' in R | CC BY-SA 2.5 | null | 2010-10-01T14:58:01.400 | 2015-04-22T01:00:23.067 | 2010-10-01T15:11:05.647 | 1216 | 1216 | [
"r",
"time-series",
"clustering",
"cointegration"
] |
3239 | 2 | null | 3238 | 18 | null | Another way of saying "tend to move in sympathy" is "cointegrated".
There are two standard ways of calculating [cointegration](http://en.wikipedia.org/wiki/Cointegration): Engle-Granger method and the Johansen procedure. These are covered in ["Analysis of Integrated and Cointegrated Time Series with R"](http://www.s... | null | CC BY-SA 2.5 | null | 2010-10-01T15:05:48.723 | 2010-10-01T15:05:48.723 | 2017-05-23T12:39:27.620 | -1 | 5 | null |
3240 | 2 | null | 3235 | 2 | null | They do different things. Time what you wish done. replicate() returns a vector of results of each execution of the function. The for loop does not. Therefore, they're not equivalent statements.
In addition, time a number of ways you want something done. Then you can find the most efficient method.
| null | CC BY-SA 2.5 | null | 2010-10-01T15:48:30.923 | 2010-10-02T16:06:05.113 | 2010-10-02T16:06:05.113 | 601 | 601 | null |
3241 | 2 | null | 3238 | 4 | null | Clustering time series is done fairly commonly by population dynamacists, particularily those that study insects to understand trends in outbreak and collapse. Look for work on Gypsy moth, Spruce budoworm, mountain pine beetle and larch budmoth.
For the actual clustering you can choose whatever distance metric you lik... | null | CC BY-SA 2.5 | null | 2010-10-01T16:34:37.630 | 2010-10-01T16:34:37.630 | null | null | 1475 | null |
3242 | 1 | 3246 | null | 22 | 11194 | I have some data to which I am trying to fit a trendline. I believe the data to follow a power law, and so have plotted the data on log-log axes looking for a straight line. This has resulted in an (almost) straight line and so in Excel I have added a trendline for a power law. Being a stats newb, my question is, wh... | How to measure/argue the goodness of fit of a trendline to a power law? | CC BY-SA 3.0 | null | 2010-10-01T17:04:11.523 | 2017-10-25T04:56:48.910 | 2013-02-22T09:08:14.080 | 8 | 870 | [
"goodness-of-fit",
"power-law"
] |
3243 | 2 | null | 3235 | 19 | null | For effective timing of programs, especially when you are interested in comparing alternative solutions, you need a control! A good way is to put the procedure you're timing into a function. Call the function within a timing loop. Write a stub procedure, essentially by stripping out all the code from your function a... | null | CC BY-SA 2.5 | null | 2010-10-01T17:08:15.340 | 2010-10-01T17:08:15.340 | null | null | 919 | null |
3244 | 1 | 3248 | null | 16 | 3814 | I know most of you probably feel that Google Docs is still a primitive tool. It is no Matlab or R and not even Excel. Yet, I am baffled at the power of this web based software that just uses the operating capability of a browser (and is compatible with many browsers that work very differently).
Mike Lawrence, active ... | Do some of you use Google Docs spreadsheet to conduct and share your statistical work with others? | CC BY-SA 2.5 | null | 2010-10-01T17:21:51.887 | 2018-07-14T17:48:44.927 | 2012-09-13T19:25:54.267 | 919 | 1329 | [
"software",
"computational-statistics"
] |
3246 | 2 | null | 3242 | 26 | null | See Aaron Clauset's page:
- Power-law Distributions in Empirical Data
which has links to code for fitting power laws (Matlab, R, Python, C++) as well as a paper by Clauset and Shalizi you should read first.
You might want to read Clauset's and Shalizi's blogs posts on the paper first:
- Power laws and all that ja... | null | CC BY-SA 3.0 | null | 2010-10-01T18:22:17.717 | 2017-05-08T09:03:29.043 | 2017-05-08T09:03:29.043 | 105234 | 251 | null |
3247 | 2 | null | 3244 | 19 | null | As an enthusiast user of R, bash, Python, asciidoc, (La)TeX, open source sofwtare or any un*x tools, I cannot provide an objective answer. Moreover, as I often argue against the use of MS Excel or spreadsheet of any kind (well, you see your data, or part of it, but what else?), I would not contribute positively to the ... | null | CC BY-SA 4.0 | null | 2010-10-01T18:31:56.090 | 2018-07-14T17:48:44.927 | 2018-07-14T17:48:44.927 | 79696 | 930 | null |
3248 | 2 | null | 3244 | 12 | null | My main use for google spreadsheets have been with google forms, for collecting data, and then easily importing it into R. Here is a post I wrote about it half a year ago:
[Google spreadsheets + google forms + R = Easily collecting and importing data for analysis](http://www.r-statistics.com/2010/03/google-spreadsheet... | null | CC BY-SA 2.5 | null | 2010-10-01T19:23:05.050 | 2010-10-01T19:23:05.050 | null | null | 253 | null |
3249 | 1 | null | null | 11 | 3497 | I want to forecast retail items (by week) using exponential smoothing. I'm stuck right now in how to calculate, store, and apply the sesonality indexes.
The problem is that all examples I've found deal with a sort of simple seasonality. In my case I have the following problems:
1. Seasons don't occur on the same w... | Calculation of seasonality indexes for complex seasonality | CC BY-SA 2.5 | null | 2010-10-01T20:05:58.550 | 2010-10-03T03:51:53.697 | 2010-10-03T03:39:27.527 | 159 | 1479 | [
"time-series",
"seasonality"
] |
3250 | 2 | null | 3235 | 4 | null | Regarding which timing metric to use, I can not add to the other responders.
Regarding the function to use, I like using the ?benchmark from the [rbenchmark package](http://cran.r-project.org/web/packages/rbenchmark/index.html).
| null | CC BY-SA 2.5 | null | 2010-10-01T20:51:10.613 | 2010-10-01T20:51:10.613 | null | null | 253 | null |
3251 | 2 | null | 3249 | 1 | null | A simple fix would be to include events dummies in your specification:
$(1) \hat{y_t}=\lambda_1 y_{t-1}+...+\lambda_k y_{t-k}+\phi_1 D_{t,1}+\phi_m D_{t,m}$
where $D_{t,m}$ is an indicator taking value $1$ if week $t$ has event $m$ (say Mardi gras) and 0 otherwise, for all $m$ events you deem important.
The first part... | null | CC BY-SA 2.5 | null | 2010-10-01T20:54:45.623 | 2010-10-02T07:42:40.397 | 2010-10-02T07:42:40.397 | 603 | 603 | null |
3252 | 1 | 3254 | null | 29 | 2865 | Exploratory data analysis (EDA) often leads to explore other "tracks" that do not necessarily belong to the initial set of hypotheses. I face such a situation in the case of studies with a limited sample size and a lot of data gathered through different questionnaires (socio-demographics data, neuropsychological or med... | How to cope with exploratory data analysis and data dredging in small-sample studies? | CC BY-SA 2.5 | null | 2010-10-01T21:52:02.977 | 2023-04-06T10:07:11.423 | 2010-10-25T06:50:55.937 | 449 | 930 | [
"multiple-comparisons",
"epidemiology",
"small-sample",
"exploratory-data-analysis"
] |
3253 | 2 | null | 3038 | 0 | null | Bootstrap differences (e.g. the difference between the means) between the 2 sample groups and check for statistical significance. A more detailed description of this approach, albeit in a different context, can be found here [http://www.automated-trading-system.com/a-different-application-of-the-bootstrap/](http://www.... | null | CC BY-SA 2.5 | null | 2010-10-01T23:13:10.210 | 2010-10-01T23:13:10.210 | null | null | 226 | null |
3254 | 2 | null | 3252 | 12 | null | I think the main thing is to be honest when reporting such results that they were unexpected findings from EDA and not part of the initial analysis plan based on an a priori hypothesis. Some people like to label such results 'hypothesis generating': e.g. the [first hit](http://jco.ascopubs.org/content/23/30/7512.short)... | null | CC BY-SA 2.5 | null | 2010-10-01T23:19:38.370 | 2010-10-02T07:49:14.927 | 2010-10-02T07:49:14.927 | 449 | 449 | null |
3255 | 2 | null | 3249 | 7 | null | For the kinds of seasonality you describe, the dummy variable approach is probably best. However, this is easier to handle in an ARIMA framework than an exponential smoothing framework.
\begin{aligned}
y_t &= a + b_1D_{t,1} + \cdots + b_mD_{t,m} + N_t\\
N_t &\sim \text{ARIMA}
\end{aligned}
where each $D_{t,k}$ variabl... | null | CC BY-SA 2.5 | null | 2010-10-01T23:54:36.450 | 2010-10-03T03:51:53.697 | 2010-10-03T03:51:53.697 | 159 | 159 | null |
3256 | 2 | null | 3207 | 2 | null | With a small modification, the first version will be far more efficient. As it's written, se.est is a random function. Even at the same arguments, its value will change each time because of rnorm. This will mess up optimize. You should use the same random numbers each time se.est is called. Here's one way:
```
e <- c(r... | null | CC BY-SA 2.5 | null | 2010-10-02T00:22:45.550 | 2010-10-02T00:22:45.550 | null | null | 1229 | null |
3257 | 2 | null | 3238 | 27 | null | In data streaming and mining of time series databases, a common approach is to transform the series to a symbolic representation, then use a similarity metric, such as Euclidean distance, to cluster the series. The most popular representations are SAX (Keogh & Lin) or the newer iSAX (Shieh & Keogh):
- Symbolic Aggreg... | null | CC BY-SA 2.5 | null | 2010-10-02T05:13:39.340 | 2010-10-02T05:13:39.340 | null | null | 251 | null |
3259 | 1 | 19865 | null | 22 | 3704 | This question is about estimating cut-off scores on a multi-dimensional screening questionnaire to predict a binary endpoint, in the presence of correlated scales.
I was asked about the interest of controlling for associated subscores when devising cut-off scores on each dimension of a measurement scale (personality t... | Adjusting for covariates in ROC curve analysis | CC BY-SA 2.5 | null | 2010-10-02T09:29:19.373 | 2011-12-15T14:11:44.723 | 2010-11-09T13:22:04.150 | 930 | 930 | [
"epidemiology",
"roc"
] |
3260 | 1 | null | null | 3 | 237 | I have a few (5-6) data sets, each is a function of time, with the time span the same between datasets. These datasets are all statistics of various perspectives of something (the partitions of a graph), and I am trying to find points in time which show interesting changes across the different datasets. Normally thes... | Combining many datasets to increase confidence | CC BY-SA 2.5 | null | 2010-10-02T12:32:01.853 | 2010-11-01T03:15:58.853 | null | null | 809 | [
"multiple-comparisons",
"multivariate-analysis"
] |
3261 | 1 | 3274 | null | 5 | 540 | Suppose you had a method for estimating the population covariance of a vector-valued random variable given observations of that random variable, say $f(Z) \rightarrow C$, where the rows of $Z$ are observations of the random variable. Can one abuse this process to perform a least squares regression $y = x^T\beta + \epsi... | Using Covariance Estimator to Perform Linear Regression? | CC BY-SA 2.5 | null | 2010-10-02T17:05:12.987 | 2010-10-03T04:21:50.877 | 2010-10-03T04:21:50.877 | 795 | 795 | [
"regression",
"algorithms",
"missing-data",
"covariance-matrix"
] |
3262 | 1 | null | null | 34 | 6169 | I am about to help teach statistics to medical students this semester.
I've heard many horror stories about the fear of these students from learning statistics.
Can anyone suggest what to do with this fear? (Either links to people who are discussing this, or offer suggestions from your own experience)
| How to teach students who fear statistics? | CC BY-SA 2.5 | null | 2010-10-02T17:06:33.500 | 2017-01-25T18:33:36.700 | 2010-10-02T17:39:01.153 | null | 253 | [
"teaching"
] |
3263 | 2 | null | 328 | 0 | null | Kennedy's [Guide to Econometrics](http://rads.stackoverflow.com/amzn/click/1405182571) is a good survey of techniques in econometrics--not detailed enough to get your hands dirty, but very good for discovering what techniques are being used.
| null | CC BY-SA 2.5 | null | 2010-10-02T17:17:44.863 | 2010-10-02T17:17:44.863 | null | null | 795 | null |
3264 | 2 | null | 3262 | 13 | null | Not very much about how to deal with students' fear, but Andrew Gelman wrote an excellent book, [Teaching Statistics, a bag of tricks](http://www.stat.columbia.edu/~gelman/bag-of-tricks/) (there's also some [slides](http://www.stat.columbia.edu/~gelman/presentations/smithtalk.pdf)).
I like introducing a course by talki... | null | CC BY-SA 2.5 | null | 2010-10-02T17:30:57.223 | 2010-10-02T18:33:09.533 | 2010-10-02T18:33:09.533 | 930 | 930 | null |
3265 | 2 | null | 3262 | 9 | null | This is a topic that would be of interest to members of the [Isolated Statisticians](http://www.lawrence.edu/fast/jordanj/isostat.html) group in the ASA. You are likely to get many useful responses from experienced teachers there, so I'll limit what I share here.
It's useful to understand where your students are comin... | null | CC BY-SA 2.5 | null | 2010-10-02T18:10:23.917 | 2010-10-02T18:10:23.917 | null | null | 919 | null |
3266 | 2 | null | 3180 | 7 | null | I like the partial identification approach to missing data of Manski. The basic idea is to ask: given all possible values the missing data could have, what is the set of values that the estimated parameters could take? This set might be very large, in which case you could consider restricting the distribution of the mi... | null | CC BY-SA 2.5 | null | 2010-10-02T19:05:07.083 | 2010-10-02T19:05:07.083 | null | null | 1229 | null |
3267 | 2 | null | 3262 | 16 | null | Try to personalize statistics. To show why understanding its concepts (even though they will forget the math, acknowledge it) is useful to them. For instance, how to interpret breast cancer test results. To quote from [http://yudkowsky.net/rational/bayes](http://yudkowsky.net/rational/bayes):
>
Here's a story problem ... | null | CC BY-SA 3.0 | null | 2010-10-02T19:21:19.000 | 2017-01-25T16:31:43.393 | 2017-01-25T16:31:43.393 | 137032 | 840 | null |
3268 | 1 | null | null | 9 | 1421 | I have around ten groups (of companies). Each group is connected with each other group. The data I have is representing the strength of the connection. Imagine it's the number of times someone from group A sent an email to group B.
The strength of a connection can be 0. There are two connections between two groups, A-B... | Visualization of connections between groups | CC BY-SA 3.0 | null | 2010-10-02T19:59:17.413 | 2017-10-06T09:50:48.220 | 2017-10-06T09:50:48.220 | 11887 | 1489 | [
"data-visualization",
"networks"
] |
3269 | 2 | null | 3268 | 3 | null | [Gephi](http://gephi.org/) is pretty good for visualization directed or undirected graphs/networks. Another option might be [Walrus](http://www.caida.org/tools/visualization/walrus/).
| null | CC BY-SA 2.5 | null | 2010-10-02T20:17:39.443 | 2010-10-02T20:17:39.443 | null | null | 251 | null |
3270 | 1 | 3278 | null | 21 | 16224 | I've been searching the internet far and wide... I have yet to find a really good overview of how to interpret 2D correspondence analysis plots. Could someone offer some advice on interpreting the distances between points?
Perhaps an example would help, here is a plot that's found on many of the websites I've seen tha... | Interpreting 2D correspondence analysis plots | CC BY-SA 3.0 | null | 2010-10-02T22:12:51.957 | 2016-06-28T22:25:22.003 | 2011-06-15T19:42:20.767 | 930 | 776 | [
"interpretation",
"correspondence-analysis",
"biplot"
] |
3271 | 1 | 3284 | null | 8 | 5669 | I have seen a few queries on clustering in time series and specifically on clustering, but I don't think they answer my question.
Background: I want to cluster genes in a time course experiment in yeast. There are four time points say: t1 t2 t3 and t4 and total number of genes G. I have the data in form a matrix M i... | Clustering genes in a time course experiment | CC BY-SA 2.5 | null | 2010-10-02T22:16:46.023 | 2010-10-12T23:32:12.033 | 2010-10-04T09:04:46.327 | 8 | 1307 | [
"r",
"machine-learning",
"clustering",
"microarray"
] |
3272 | 2 | null | 3262 | 14 | null | I agree that making statistics personal/relevant is important, but that's not ultimately going to dispel the fear of the student. I think how the student feels about something often has more to do with the personality of the person teaching it, and how comfortable that person feels in the classroom, even when teaching ... | null | CC BY-SA 2.5 | null | 2010-10-02T22:51:17.683 | 2010-10-02T22:51:17.683 | null | null | 1490 | null |
3273 | 2 | null | 3262 | 3 | null | "Decision making in the face of uncertainty" sounds a lot more interesting than "statistics" even though that's essentially what statistics is about. Maybe you could lead with the decision-making aspect to build motivation for the course.
| null | CC BY-SA 2.5 | null | 2010-10-02T23:58:10.383 | 2010-10-02T23:58:10.383 | null | null | 319 | null |
3274 | 2 | null | 3261 | 3 | null | your "trick" seems to be the solution to the [so-called] normal equations for multiple regression - which is the usual least-squares answer in multiple regression.
as for missing data - what $f$ do you have in mind that knows how to get $C$ in that case?
there are methods like imputation for filling in missing values... | null | CC BY-SA 2.5 | null | 2010-10-03T04:14:41.910 | 2010-10-03T04:14:41.910 | null | null | 1112 | null |
3275 | 1 | 3357 | null | 3 | 12542 | Let's say I'm playing 10 "games". For each game, I know the probability of winning, the probability of tying, and the probability of losing.
From these values, I can calculate the probability of winning X games, the probability of losing X games, and the probability of tying X games (for X = 0 to 10).
I'm just trying ... | Probability of Game Outcomes (with ties!) | CC BY-SA 2.5 | null | 2010-10-03T04:42:40.847 | 2022-07-18T19:53:27.317 | 2019-06-29T07:58:41.570 | 3277 | null | [
"probability",
"multinomial-distribution",
"games",
"ties"
] |
3276 | 1 | 3279 | null | 7 | 549 | Suppose I have some i.i.d. data $x_1, \ldots, x_n \sim N(\mu, \sigma^2)$, where $\sigma^2$ is fixed and $\mu$ is unknown, and I want to estimate $\mu$.
Instead of simply giving the MLE of $\mu = \bar{x}$, one could estimate
(1) $\mu = \lambda \mu_0 + (1 - \lambda) \bar{x},$
for some "prior best guess" $\mu_0$. This als... | Regularization and Mean Estimation | CC BY-SA 2.5 | null | 2010-10-03T05:01:18.027 | 2010-10-03T16:53:49.277 | null | null | 1106 | [
"bayesian",
"estimation",
"mean"
] |
3277 | 2 | null | 3268 | 3 | null | A quick couple of thoughts:
- I've used multidimensional scaling to visualise connections between team members (i.e., a weighted network). Nodes with stronger connections then appear closer in the figure. Here's some resources for implementing in R.
- You could present a standard graph where line thickness is based o... | null | CC BY-SA 2.5 | null | 2010-10-03T06:35:56.690 | 2010-10-03T06:35:56.690 | null | null | 183 | null |
3278 | 2 | null | 3270 | 25 | null | First, there are different ways to construct so-called [biplots](http://en.wikipedia.org/wiki/Biplot) in the case of correspondence analysis. In all cases, the basic idea is to find a way to show the best 2D approximation of the "distances" between row cells and column cells. In other words, we seek a hierarchy (we als... | null | CC BY-SA 2.5 | null | 2010-10-03T10:14:06.133 | 2010-10-04T20:40:05.720 | 2010-10-04T20:40:05.720 | 930 | 930 | null |
3279 | 2 | null | 3276 | 3 | null | Sure, it would be equivalent to the following ridge-like optimization problem:
$\underset{\mu\in\mathbb{R}|\mu_0,\lambda\geq0}{\min} ||x_i-\mu-\mu_0||_2+\lambda\mu^2$
For $\lambda=0$, $\mu+\mu_0$ goes to the OLS solution (i.e. $\bar{x}$), for $\lambda=\infty$, it shrinks to $\mu_0$.
| null | CC BY-SA 2.5 | null | 2010-10-03T11:09:10.000 | 2010-10-03T12:15:18.037 | 2010-10-03T12:15:18.037 | 603 | 603 | null |
3280 | 2 | null | 3276 | 3 | null | Ridge regression (Hoerl and Kennard, 1988) was initially developed to overcome singularities when inverting $X^tX$ (by adding $\lambda$ to its diagonal elements). Thus, the regularization in this case consists in working with a vc matrix $(X^tX-\lambda I)^{-1}$. This L2 penalization leads to "better" predictions than w... | null | CC BY-SA 2.5 | null | 2010-10-03T11:12:14.313 | 2010-10-03T16:53:49.277 | 2010-10-03T16:53:49.277 | 930 | 930 | null |
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