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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2049 | 2 | null | 1908 | 0 | null | [IEEE World Congress on Computational Intelligence](http://www.wcci2010.org/topics/ijcnn-2010). Note that it is the link for 2010 conference.
| null | CC BY-SA 2.5 | null | 2010-08-23T14:55:35.160 | 2010-08-23T14:55:35.160 | null | null | 976 | null |
2050 | 2 | null | 1908 | 0 | null | [International Conference on Artificial Neural Networks](http://delab.csd.auth.gr/icann2010/). Note that the link is for the 2010 conference.
| null | CC BY-SA 2.5 | null | 2010-08-23T14:58:22.317 | 2010-08-23T14:58:22.317 | null | null | 976 | null |
2051 | 2 | null | 1908 | 1 | null | [Artificial Intelligence In Medicine](http://aimedicine.info/aime/) (AIME), odd years starting from 1985.
| null | CC BY-SA 2.5 | null | 2010-08-23T15:35:18.580 | 2010-08-23T15:35:18.580 | null | null | 930 | null |
2053 | 2 | null | 1844 | 11 | null | I think I figured out the answer (to my own question). If the assumption of proportional hazards is true, the two methods give similar estimates of the hazard ratio. The discrepancy I found in one particular example, I now think, is due to the fact that that assumption is dubious.
If the assumption of proportional haz... | null | CC BY-SA 2.5 | null | 2010-08-23T18:03:15.097 | 2010-08-23T21:10:58.187 | 2010-08-23T21:10:58.187 | 25 | 25 | null |
2054 | 2 | null | 1927 | 6 | null | The Tukey halfspace median can be extended to >2 dimensions using DEEPLOC, an algorithm due to Struyf and Rousseeuw; see [here](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.2052) for details.
The algorithm is used to approximate the point of greatest depth efficiently; naive methods which attempt to deter... | null | CC BY-SA 2.5 | null | 2010-08-23T18:34:05.990 | 2010-08-23T18:34:05.990 | null | null | 1068 | null |
2055 | 2 | null | 1799 | 2 | null | As to your suggestion of using Multi-level models in this and the other thread, I see no benefit of approaching your analysis in this manner over repeated measures ANOVA. MLM are simply an extension of OLS regression, and offer an explicit framework to model group level (often referred to as "contextual") effects on lo... | null | CC BY-SA 2.5 | null | 2010-08-23T19:47:39.243 | 2010-08-24T01:49:46.973 | 2010-08-24T01:49:46.973 | 1036 | 1036 | null |
2056 | 2 | null | 1399 | 6 | null | According to Davison and Hinckley ("Bootstrap methods and their application", 1997, Section 3.8), the third algorithm is conservative. They advocate a fourth approach: simply resampling the subjects.
| null | CC BY-SA 2.5 | null | 2010-08-23T20:39:21.760 | 2010-08-23T20:39:21.760 | null | null | 187 | null |
2057 | 2 | null | 1841 | 6 | null | I have a relatively simple solution to propose, Hugo. Because you're forthright about not being a statistician (often a plus ;-) but obviously can handle technical language, I'll take some pains to be technically clear but avoid statistical jargon.
Let's start by checking my understanding: you have six series of data... | null | CC BY-SA 2.5 | null | 2010-08-23T22:04:33.300 | 2010-08-25T13:52:20.933 | 2010-08-25T13:52:20.933 | 919 | 919 | null |
2058 | 2 | null | 213 | 12 | null | You can find candidates for "outliers" among the support points of the minimum volume bounding ellipsoid. ([Efficient algorithms](http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/cluster/html/ellipsoidhull.html) to find these points in fairly high dimensions, both exactly and approximately, were invented in a spat... | null | CC BY-SA 3.0 | null | 2010-08-23T22:41:05.283 | 2012-02-17T19:04:13.153 | 2012-02-17T19:04:13.153 | 919 | 919 | null |
2059 | 1 | null | null | 6 | 597 | It seems that MLE (via EM) is widely used in machine learning / statistics to learn the parameters of a mixture of Gaussians. I'm assuming we're given random samples from the mixture.
My question is: Are there any proven quantitative bounds on the error in terms of the number of samples (and perhaps the parameters of ... | Learning parameters of a mixture of Gaussian using MLE | CC BY-SA 2.5 | null | 2010-08-23T23:21:15.567 | 2011-03-27T16:01:57.583 | 2011-03-27T16:01:57.583 | 919 | 1072 | [
"estimation",
"normal-distribution",
"expectation-maximization",
"mixture-distribution"
] |
2060 | 2 | null | 2059 | 4 | null | EM essentially solves the maximum likelihood problem and therefore has the same properties w.r.t. sample sizes. EM for Gaussian mixture models is known to converge asymptotically to a local maximum and exhibits first order convergence (see [this paper](http://dspace.mit.edu/bitstream/handle/1721.1/7195/AIM-1520.pdf;jse... | null | CC BY-SA 2.5 | null | 2010-08-24T00:27:18.560 | 2010-08-24T01:24:08.150 | 2010-08-24T01:24:08.150 | 881 | 881 | null |
2061 | 1 | null | null | 13 | 1711 | When lecturing statistics, it can be useful to incorporate the occasional short video.
My first thoughts included:
- Animations and visualisations of statistical concepts
- Stories regarding the application of a particular technique
- Humorous videos that have relevance to a statistical idea
- Interviews with a s... | Short online videos that could assist a lecture on statistics | CC BY-SA 2.5 | null | 2010-08-24T02:48:08.290 | 2018-10-17T15:53:02.093 | 2010-09-17T08:49:22.640 | 183 | 183 | [
"teaching",
"references"
] |
2062 | 2 | null | 2061 | 11 | null | Link:
[https://yihui.name/animation/](https://yihui.name/animation/)
A big list of animation "clips" (gif's or other formats), through the use of the "[animation](http://cran.r-project.org/web/packages/animation/index.html)" package (R). Including the following topics:
Topics(in each of them there are 1 or more animat... | null | CC BY-SA 4.0 | null | 2010-08-24T02:57:23.123 | 2018-10-17T15:53:02.093 | 2018-10-17T15:53:02.093 | 103520 | 253 | null |
2063 | 2 | null | 1873 | 1 | null | Perhaps I'm misunderstanding the OP, but shouldn't the test be for 8 successes (of not stubbing the fellow's toe) and 7 failures, for a total of 15 trials? And shouldn't the comparison be to a probability of 2/3 (2000 incidents of not stubbing a toe relative to the 1000 observed ascents)? This, of course, is taking the... | null | CC BY-SA 2.5 | null | 2010-08-24T06:06:54.557 | 2010-08-24T06:06:54.557 | null | null | 401 | null |
2064 | 2 | null | 1998 | 5 | null | In the social sciences, we often call this issue "post treatment bias." If you are considering the effect of some treatment (your independent variable), including variables that arise after treatment (in a causal sense), then your estimate of the treatment effect can be biased. If you include these variables, then you ... | null | CC BY-SA 2.5 | null | 2010-08-24T06:38:43.993 | 2010-08-24T06:38:43.993 | null | null | 401 | null |
2065 | 2 | null | 1844 | 3 | null | I thought I'd stumbled across a web site and reference that deals exactly with this question:
[http://www.graphpad.com/faq/viewfaq.cfm?faq=1226](http://www.graphpad.com/faq/viewfaq.cfm?faq=1226)
Start from "The two methods compared".
The site references the Berstein paper ars linked (above):
[http://www.jstor.org/stabl... | null | CC BY-SA 2.5 | null | 2010-08-24T06:49:15.703 | 2010-08-24T06:49:15.703 | null | null | 521 | null |
2066 | 1 | 2074 | null | 15 | 7972 | Is there a numerically stable way to calculate values of a [beta distribution](http://en.wikipedia.org/wiki/Beta_distribution) for large integer alpha, beta (e.g. alpha,beta > 1000000)?
Actually, I only need a 99% confidence interval around the mode, if that somehow makes the problem easier.
Add: I'm sorry, my question... | How can I (numerically) approximate values for a beta distribution with large alpha & beta | CC BY-SA 2.5 | null | 2010-08-24T09:48:08.093 | 2023-02-11T14:22:18.387 | 2010-10-06T14:29:31.523 | 8 | 956 | [
"confidence-interval",
"algorithms",
"beta-distribution"
] |
2067 | 1 | 2138 | null | 14 | 5404 | I am currently finishing a paper and stumbled upon [this question from yesterday](https://stats.stackexchange.com/questions/2024/plotting-standard-errors) which led me to pose the same question to myself. Is it better to provide my graph with the actual standard error from the data or the one estimated from my ANOVA?
A... | Follow up: In a mixed within-between ANOVA plot estimated SEs or actual SEs? | CC BY-SA 2.5 | null | 2010-08-24T10:05:42.533 | 2012-02-21T08:49:36.340 | 2017-04-13T12:44:40.807 | -1 | 442 | [
"data-visualization",
"anova",
"mixed-model",
"standard-error"
] |
2068 | 2 | null | 2067 | 8 | null | You will not find a single reasonable error bar for inferential purposes with this type of experimental design. This is an old problem with no clear solution.
It seems impossible to have the estimate SE's you have here. There are two main kinds of error in such a design, the between and within S error. They are usua... | null | CC BY-SA 3.0 | null | 2010-08-24T10:35:37.933 | 2012-02-21T08:49:36.340 | 2012-02-21T08:49:36.340 | 601 | 601 | null |
2069 | 2 | null | 2067 | 7 | null | This looks like a very nice experiment, so congratulations!
I agree with John Christie, it is a mixed model, but provided it can be properely specified in an ANOVA design (& is balanced) I don't see why it can't be so formulated. Two factor within and 1-factor between subjects, but the between subjects factor (inductiv... | null | CC BY-SA 2.5 | null | 2010-08-24T11:21:58.323 | 2010-08-24T11:39:14.513 | 2010-08-24T11:39:14.513 | 521 | 521 | null |
2070 | 2 | null | 2067 | 3 | null | Lately I've been using mixed effects analysis, and in attempting to develop an accompanying visual data analysis approach I've been using bootstrapping ([see my description here](https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004369.html)), which yields confidence intervals that are not susceptible to the wit... | null | CC BY-SA 2.5 | null | 2010-08-24T11:32:58.983 | 2010-08-24T11:32:58.983 | null | null | 364 | null |
2071 | 2 | null | 2066 | 3 | null | A quick graphical experiment suggests that the beta distribution looks very like a normal distribution when alpha and beta are both very large. By googling "beta distribution limit normal" i found [http://nrich.maths.org/discus/messages/117730/143065.html?1200700623$^\dagger$](http://nrich.maths.org/discus/messages/117... | null | CC BY-SA 4.0 | null | 2010-08-24T12:38:01.413 | 2023-02-11T10:26:41.770 | 2023-02-11T10:26:41.770 | 362671 | 449 | null |
2072 | 1 | null | null | 17 | 1633 | It isn't clear to me how to calculate cointegration with irregular time series (ideally using [the Johansen test](http://en.wikipedia.org/wiki/Johansen_test) with VECM). My initial thought would be to regularize the series and interpolate missing values, although that may bias the estimation.
Is there any literature o... | Does a cointegration model exist for irregularly spaced time series? | CC BY-SA 2.5 | null | 2010-08-24T13:07:33.350 | 2019-07-20T17:32:42.237 | 2019-07-20T17:32:42.237 | 11887 | 5 | [
"time-series",
"cointegration",
"unevenly-spaced-time-series"
] |
2073 | 2 | null | 2066 | 2 | null | I am going to infer you want an interval $[l,r]$ such that the probability that a random draw from the Beta RV is in the interval with probability 0.99, with bonus points for $l$ and $r$ being symmetric around the mode. By [Gauss' Inequality](http://en.wikipedia.org/wiki/Gauss%27s_inequality) or the Vysochanskii-Petuni... | null | CC BY-SA 2.5 | null | 2010-08-24T15:06:00.023 | 2010-08-24T15:06:00.023 | null | null | 795 | null |
2074 | 2 | null | 2066 | 13 | null | A Normal approximation works extremely well, especially in the tails. Use a mean of $\alpha/(\alpha+\beta)$ and a variance of $\frac{\alpha\beta}{(\alpha+\beta)^{2} (1+\alpha+\beta)}$. For example, the absolute relative error in the tail probability in a tough situation (where skewness might be of concern) such as $\... | null | CC BY-SA 4.0 | null | 2010-08-24T15:43:19.670 | 2023-02-11T10:24:33.733 | 2023-02-11T10:24:33.733 | 362671 | 919 | null |
2075 | 2 | null | 927 | 3 | null | I also just realized the [freakonomics has a podcast](http://itunes.apple.com/us/podcast/freakonomics-radio/id354668519)
| null | CC BY-SA 2.5 | null | 2010-08-24T16:17:53.823 | 2010-08-24T16:17:53.823 | null | null | 253 | null |
2076 | 2 | null | 924 | 1 | null | If you wanted to check non-parametrically for significance, you could bootstrap the confidence intervals on the ratio, or you could do a permutation test on the two classes. For example, to do the bootstrap, create two arrays: one with 3 ones and 999,997 zeros, and one with 10 ones and 999,990 zeros. Then draw with rep... | null | CC BY-SA 2.5 | null | 2010-08-24T17:08:00.637 | 2010-08-24T17:08:00.637 | null | null | 1076 | null |
2077 | 1 | 2080 | null | 51 | 61162 | Besides taking differences, what are other techniques for making a non-stationary time series, stationary?
Ordinarily one refers to a series as "[integrated of order p](http://en.wikipedia.org/wiki/Order_of_integration)" if it can be made stationary through a lag operator $(1-L)^P X_t$.
| How to make a time series stationary? | CC BY-SA 2.5 | null | 2010-08-24T18:40:42.633 | 2021-11-14T21:32:30.737 | null | null | 5 | [
"time-series",
"stationarity"
] |
2078 | 2 | null | 1927 | 10 | null | [Geometric median](http://en.wikipedia.org/wiki/Geometric_median) is the point with the smallest average euclidian distance from the samples
| null | CC BY-SA 2.5 | null | 2010-08-24T19:25:14.850 | 2010-08-24T19:25:14.850 | null | null | 511 | null |
2079 | 1 | null | null | 3 | 118 | There seems to be a large body of applied research where distribution q is picked to minimize [KL(q,p)](http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) where p is empirical distribution. Are there theoretical reasons to prefer this estimator? For instance, a theoretical reason to prefer MLE for estima... | What's good about I-projections? | CC BY-SA 2.5 | null | 2010-08-24T20:09:45.430 | 2018-10-07T20:31:51.977 | 2018-10-07T20:31:51.977 | 11887 | 511 | [
"estimation",
"kullback-leibler"
] |
2080 | 2 | null | 2077 | 23 | null | De-trending is fundamental. This includes regressing against covariates other than time.
Seasonal adjustment is a version of taking differences but could be construed as a separate technique.
Transformation of the data implicitly converts a difference operator into something else; e.g., differences of the logarithms a... | null | CC BY-SA 2.5 | null | 2010-08-24T21:27:09.350 | 2010-08-24T21:27:09.350 | null | null | 919 | null |
2081 | 2 | null | 1681 | 2 | null | >
in that notation, I'm looking for epsilon as a function of m
If I have not misunderstood, this indicates you want to solve an equation in $q~ (= p +\epsilon)$ of the form
$$q \log(p/q) + (1-q) \log((1-p)/(1-q)) = D(q\,\Vert\, p) = y;\ y \lt 0,$$
given $p$ and $y.$ ($y = \log(p)/m$ reveals the $m$-dependence.)
It ... | null | CC BY-SA 4.0 | null | 2010-08-24T22:19:51.203 | 2023-02-11T12:54:06.857 | 2023-02-11T12:54:06.857 | 919 | 919 | null |
2082 | 2 | null | 2077 | 5 | null | Difference with another series. i.e. Brent oil prices are not stationary,
but the spread brent-light sweet crude might be. A more risky proposition for
forecasting is to bet on the existence of a co integration relationship
with another time series.
| null | CC BY-SA 4.0 | null | 2010-08-24T22:38:15.263 | 2021-11-14T21:32:30.737 | 2021-11-14T21:32:30.737 | 603 | 603 | null |
2084 | 2 | null | 1469 | 3 | null | [Asymptotic Statistics](https://www.cambridge.org/core/books/asymptotic-statistics/A3C7DAD3F7E66A1FA60E9C8FE132EE1D) by A. W. van der Vaart seems to serve the purpose.
| null | CC BY-SA 4.0 | null | 2010-08-25T00:46:17.397 | 2022-11-29T18:35:55.963 | 2022-11-29T18:35:55.963 | 362671 | 511 | null |
2085 | 1 | 2115 | null | 6 | 2092 | I am modelling a nested mixed-effects model with just the intercept in the random part, of the form:
```
fit4<-lme(fixed = Stdquad~factor(LayoutN)+factor(nCHIPS.fixed), random = ~1|Class.Ordered/student)
```
When trying to check the assumption on the independent, identically distributed (iid) random effects in SPlus u... | Checking assumptions for random effects in nested mixed-effects models in R / S-Plus | CC BY-SA 2.5 | null | 2010-08-25T01:57:42.433 | 2013-07-15T23:20:40.837 | 2013-07-15T23:20:40.837 | 22468 | 108 | [
"r",
"mixed-model",
"random-effects-model",
"error-message"
] |
2086 | 1 | 2087 | null | 12 | 9538 | I'm looking for correlations between the answers to different questions in a survey ("umm, let's see if answers to question 11 correlate with those of question 78"). All answers are categorical (most of them range from "very unhappy" to "very happy"), but a few have a different set of answers. Most of them can be consi... | Quickly evaluate (visually) correlations between ordered categorical data in R? | CC BY-SA 2.5 | null | 2010-08-25T03:16:23.030 | 2010-08-25T04:50:17.533 | 2010-08-25T04:05:22.290 | 840 | 840 | [
"r",
"correlation",
"categorical-data",
"data-visualization"
] |
2087 | 2 | null | 2086 | 19 | null | Another good visualization of correlation is offered by the [corrplot](http://cran.r-project.org/web/packages/corrplot/index.html) package, giving you things like this:
It is a great package.
Also have a look at the [answer here](https://stats.stackexchange.com/questions... | null | CC BY-SA 2.5 | null | 2010-08-25T03:32:04.410 | 2010-08-25T03:32:04.410 | 2017-04-13T12:44:49.837 | -1 | 253 | null |
2088 | 2 | null | 2086 | 3 | null | A couple of additional plotting ideas are:
- Sunflower plot
- Scatter plot with a jitter using base graphics or ggplot2
| null | CC BY-SA 2.5 | null | 2010-08-25T04:50:17.533 | 2010-08-25T04:50:17.533 | null | null | 183 | null |
2089 | 2 | null | 1595 | 97 | null | First, let me say I agree with John D Cook's answer: Python is not a Domain Specific Language like R, and accordingly, there is a lot more you'll be able to do with it further down the road. Of course, R being a DSL means that the latest algorithms published in JASA will almost certainly be in R. If you are doing mostl... | null | CC BY-SA 2.5 | null | 2010-08-25T05:19:46.727 | 2010-08-25T17:42:00.907 | 2010-08-25T17:42:00.907 | 1080 | 1080 | null |
2090 | 2 | null | 2061 | 6 | null | ASA Sections on: Statistical Computing Statistical Graphics has a video library:
- http://stat-graphics.org/movies/
It contains a large number of interesting videos relevant to statistical computing and graphics. The videos go back as far as the 1960s.
| null | CC BY-SA 2.5 | null | 2010-08-25T05:38:05.883 | 2010-08-25T05:38:05.883 | null | null | 183 | null |
2091 | 1 | 2103 | null | 4 | 1861 | X is uniform random variable in [0,1] and Y=1-X. How do I calculate the distribution function F(X,Y)? I can see that Y is also uniformly distributed and can draw the intervals. But I am unable to compute the distribution function when x+y>1 & x,y in [0,1]. How do I compute it for this specific case?
| Computing probability distribution function for uniform random variables and Y=1-X | CC BY-SA 2.5 | null | 2010-08-25T08:22:06.220 | 2010-10-14T14:44:02.327 | 2010-10-14T02:31:58.780 | 919 | 862 | [
"distributions",
"probability",
"copula"
] |
2092 | 1 | 2094 | null | 105 | 255432 | The waiting times for poisson distribution is an exponential distribution with parameter lambda. But I don't understand it. Poisson models the number of arrivals per unit of time for example. How is this related to exponential distribution? Lets say probability of k arrivals in a unit of time is P(k) (modeled by poisso... | Relationship between poisson and exponential distribution | CC BY-SA 2.5 | null | 2010-08-25T08:33:14.610 | 2020-05-27T02:16:09.500 | 2010-09-18T21:58:05.727 | 930 | 862 | [
"distributions",
"poisson-distribution",
"exponential-distribution"
] |
2093 | 1 | 2097 | null | 3 | 4780 | I've implemented the [Marsaglia polar method](http://en.wikipedia.org/wiki/Marsaglia_polar_method) to generate random numbers that are normally distributed. Unfortunately the code as shown on this [website](http://www.taygeta.com/random/gaussian.html) returns numbers that are not within the range [0, 1).
Code:
```
... | How to make Marsaglia polar method return values [0, 1)? | CC BY-SA 2.5 | null | 2010-08-25T09:32:22.513 | 2010-08-25T21:15:38.573 | 2017-04-13T12:44:33.977 | -1 | 937 | [
"random-generation",
"normal-distribution"
] |
2094 | 2 | null | 2092 | 99 | null | I will use the following notation to be as consistent as possible with the wiki (in case you want to go back and forth between my answer and the wiki definitions for the [poisson](http://en.wikipedia.org/wiki/Poisson_distribution) and [exponential](http://en.wikipedia.org/wiki/Exponential_distribution).)
$N_t$: the num... | null | CC BY-SA 3.0 | null | 2010-08-25T09:43:51.450 | 2017-08-10T23:14:14.070 | 2017-08-10T23:14:14.070 | 44024 | null | null |
2095 | 2 | null | 2091 | 4 | null | $F(x,y) = P(X \le x, Y \le y)$
Using the fact that $Y= 1-X$ and after simplifying, the above can be re-written as:
$F(x,y) = P(1-y \le X \le x)$
Since, $X$ is a uniform between 0 and 1, it follows that:
$F(x,y) = x+y-1$
Update
The above is a bit sloppy as I did not specify the domain of the cdf where it is non-zero app... | null | CC BY-SA 2.5 | null | 2010-08-25T10:01:31.670 | 2010-10-14T14:44:02.327 | 2020-06-11T14:32:37.003 | -1 | null | null |
2096 | 2 | null | 2092 | 57 | null | For a Poisson process, hits occur at random independent of the past, but with a known long term average rate $\lambda$ of hits per time unit. The Poisson distribution would let us find the probability of getting some particular number of hits.
Now, instead of looking at the number of hits, we look at the random variabl... | null | CC BY-SA 4.0 | null | 2010-08-25T10:58:52.970 | 2019-10-12T11:50:39.457 | 2019-10-12T11:50:39.457 | 7290 | 339 | null |
2097 | 2 | null | 2093 | 7 | null | It is just impossible, since normal distribution has nonzero probability for the whole $R$. Of course because of the numerical issues it would be not the case for Marsaglia method (this obviously means that it is not creating normally distributed numbers), yet this numerical range is so huge that it is nonsense to scal... | null | CC BY-SA 2.5 | null | 2010-08-25T11:01:38.983 | 2010-08-25T21:15:38.573 | 2010-08-25T21:15:38.573 | null | null | null |
2098 | 2 | null | 2093 | 2 | null | Based on the update you want to generate truncated normal random variables. Scaling based on the maximum and minimum will 'destroy' the normal distribution properties of the draws. Instead, you should consider using one of the two methods described below:
[Rejection Sampling](http://en.wikipedia.org/wiki/Rejection_samp... | null | CC BY-SA 2.5 | null | 2010-08-25T12:25:39.347 | 2010-08-25T12:25:39.347 | null | null | null | null |
2099 | 1 | 2100 | null | 7 | 4742 | Is there any free desktop software for [treemapping](http://en.wikipedia.org/wiki/Treemapping)? [Every one I've found](http://en.wikipedia.org/wiki/List_of_treemapping_software) appears to have a commercial license. [Many Eyes](http://manyeyes.alphaworks.ibm.com/manyeyes/) is free and has treemapping features but what ... | Free treemapping software | CC BY-SA 2.5 | null | 2010-08-25T13:10:22.887 | 2013-10-02T05:11:19.890 | null | null | 666 | [
"data-visualization",
"software"
] |
2100 | 2 | null | 2099 | 5 | null | [Flowing Data has a tutorial](http://flowingdata.com/2010/02/11/an-easy-way-to-make-a-treemap/) on how to use the `map.market` function in the `portfolio` package in R.
| null | CC BY-SA 2.5 | null | 2010-08-25T13:15:48.387 | 2010-08-25T13:15:48.387 | null | null | 183 | null |
2101 | 2 | null | 2099 | 6 | null | Protovis has an [nice treemap layout](http://vis.stanford.edu/protovis/ex/treemap.html). I could try to add this into [webvis](http://cran.r-project.org/web/packages/webvis/index.html) if you want to create it from R, but it isn't currently an option.
| null | CC BY-SA 2.5 | null | 2010-08-25T13:31:32.787 | 2010-08-25T13:31:32.787 | null | null | 5 | null |
2102 | 2 | null | 2093 | 3 | null | Another possible interpretation is that you are confusing "normal" and "uniform". A uniform variate will have a mean of 0.5 and be evenly distributed on [0, 1). If this interpretation is correct, your code simplifies to
```
return ranf()
```
| null | CC BY-SA 2.5 | null | 2010-08-25T13:36:22.150 | 2010-08-25T13:36:22.150 | null | null | 919 | null |
2103 | 2 | null | 2091 | 3 | null | I'm interpreting your question as concerning the cdf, which by definition is a function $F$ for which
$$F(x,y) - F(x,0) - F(0,y) + F(0,0) = \Pr(X \le x, Y \le y) \text{;}$$
$$F(0,0) = 0.$$
For $x + y \lt 1$, the right hand side is zero and the left hand side becomes a statement that $F$ is a bilinear function, implying... | null | CC BY-SA 2.5 | null | 2010-08-25T14:08:50.823 | 2010-10-14T02:31:24.530 | 2010-10-14T02:31:24.530 | 919 | 919 | null |
2104 | 1 | 2106 | null | 8 | 10110 | Thank you for reading. I am trying to get sphericity values for a purely within subject design. I have been unable to use ezANOVA, or Anova(). Anova works if I add a between subject factor, but I have been unable to get sphericity for a purely within subject design. Any advice?
I already read the R newsletter, fox chap... | How to get Sphericity in R for a nested within subject design? | CC BY-SA 2.5 | null | 2010-08-25T14:17:44.987 | 2010-09-07T15:44:17.460 | 2010-08-27T18:36:36.293 | 1084 | 1084 | [
"r",
"anova",
"sphericity"
] |
2105 | 2 | null | 2093 | 5 | null | You also might want to consider that perhaps you don't want a normally distributed variable on [0,1) but rather a random variable with a bell-shaped distribution naturally restricted to the [0,1) interval. If so, consider the [Beta distribution](http://en.wikipedia.org/wiki/Beta_distribution) or the [Kumaraswamy distri... | null | CC BY-SA 2.5 | null | 2010-08-25T14:43:24.377 | 2010-08-25T14:43:24.377 | null | null | 279 | null |
2106 | 2 | null | 2104 | 8 | null | Try:
```
library(ez)
ezANOVA(data=subset(p12bl, exps==1),
within=.(sucrose, citral),
wid=.(subject),
dv=.(resp)
)
```
and the equivalent aov command, minus sphericity etc, is:
```
aov(resp ~ sucrose*citral + Error(subject/(sucrose*citral)),
data= subset(p12bl, exps==1))
```
Here's the equivalent using An... | null | CC BY-SA 2.5 | null | 2010-08-25T15:22:37.273 | 2010-08-31T15:25:02.000 | 2010-08-31T15:25:02.000 | 966 | 966 | null |
2107 | 2 | null | 2099 | 2 | null | While I haven't used either of these, I have these two projects bookmarked:
- Treeviz
- JTreeMap
Both are implemented in Java, are free and open source (MIT and Apache 2.0 licenses, respectively). Both appear to be actually libraries, but seem to come with an example application.
| null | CC BY-SA 2.5 | null | 2010-08-25T16:31:08.917 | 2010-08-25T16:31:08.917 | null | null | 705 | null |
2108 | 1 | null | null | 5 | 5375 | I have a web service, which returns a dataset, and I would like to query it from the [R] statistical package. I'd like to know how (or if) this can be done and also how to load the first datatable, of this dataset, into memory.
Thanks!
EDIT: It is an XML web service
| Using [R] to connect to a Web Service | CC BY-SA 2.5 | null | 2010-08-25T16:44:21.330 | 2010-08-26T12:36:48.317 | 2010-08-26T09:52:27.760 | 428 | 428 | [
"r"
] |
2110 | 2 | null | 2108 | 3 | null | If your webservice amounts to calling a URL and getting back XML or json, then you can just use the `XML` or `rjson` packages directly for this. Possibly with `RCurl` for more elaborate session handling. See [the RCurl paper for an example](http://www.omegahat.org/RCurl/RCurlJSS.pdf). You can also look at [our overf... | null | CC BY-SA 2.5 | null | 2010-08-25T16:49:37.560 | 2010-08-26T12:36:48.317 | 2017-05-23T12:39:26.150 | -1 | 5 | null |
2111 | 1 | null | null | 10 | 2312 | For the selection of predictors in multivariate linear regression with $p$ suitable predictors, what methods are available to find an 'optimal' subset of the predictors without explicitly testing all $2^p$ subsets? In 'Applied Survival Analysis,' Hosmer & Lemeshow make reference to Kuk's method, but I cannot find the o... | Computing best subset of predictors for linear regression | CC BY-SA 2.5 | null | 2010-08-25T18:15:55.980 | 2014-07-22T17:12:30.223 | 2010-08-25T22:57:35.883 | null | 795 | [
"modeling",
"regression",
"multivariable",
"model-selection",
"feature-selection"
] |
2112 | 2 | null | 2104 | 7 | null | Did you try the `car` package, from John Fox? It includes the function `Anova()` which is very useful when working with experimental designs. It should give you corrected p-value following Greenhouse-Geisser correction and Huynh-Feldt correction. I can post a quick R example if you wonder how to use it.
Also, there is ... | null | CC BY-SA 2.5 | null | 2010-08-25T18:30:01.223 | 2010-08-25T18:30:01.223 | null | null | 930 | null |
2113 | 2 | null | 2032 | 4 | null | Go to [http://www.vaultanalytics.com/books](http://www.vaultanalytics.com/books)
They have written a book on what predictive models are, when to use what tests/models, and how to create them in Excel. I'm using it every day in my job. I think it's extremely useful.
| null | CC BY-SA 3.0 | null | 2010-08-25T18:32:36.633 | 2014-06-07T06:33:09.310 | 2014-06-07T06:33:09.310 | 27433 | null | null |
2114 | 2 | null | 2111 | 13 | null | I've never heard of Kuk's method, but the hot topic these days is L1 minimisation. The rationale being that if you use a penalty term of the absolute value of the regression coefficients, the unimportant ones should go to zero.
These techniques have some funny names: Lasso, LARS, Dantzig selector. You can read the pape... | null | CC BY-SA 2.5 | null | 2010-08-25T18:37:15.187 | 2010-08-25T18:37:15.187 | null | null | 495 | null |
2115 | 2 | null | 2085 | 6 | null | It seems you are using the `nlme` package. Maybe it would be worth trying R and the `lme4` instead, although it is not fully comparable wrt. syntax or function call.
In your case, I would suggest to specify the `level` when you called `ranef()`, see `?ranef.lme`:
level: an optional vector of positive integers givi... | null | CC BY-SA 2.5 | null | 2010-08-25T19:45:27.547 | 2010-08-25T19:45:27.547 | null | null | 930 | null |
2116 | 1 | null | null | 15 | 14183 | As someone who needs statistical knowledge but is not a formally trained statistician, I'd find it helpful to have a flowchart (or some kind of decision tree) to help me choose the correct approach to solve a particular problem (eg. "do you need this and know that and that and consider data to be normally distributed? ... | Flowcharts to help selecting the proper analysis technique and test | CC BY-SA 3.0 | null | 2010-08-25T20:09:26.997 | 2022-07-11T09:05:44.673 | 2015-12-17T22:43:53.020 | 28666 | 840 | [
"hypothesis-testing",
"data-mining",
"exploratory-data-analysis"
] |
2117 | 2 | null | 2116 | 9 | null | These are not really interactive flowcharts, but maybe this could be useful: (1) [http://j.mp/cmakYq](http://j.mp/cmakYq), (2) [http://j.mp/aaxUsz](http://j.mp/aaxUsz), and (3) [http://j.mp/bDMyAR](http://j.mp/bDMyAR).
| null | CC BY-SA 2.5 | null | 2010-08-25T20:17:24.127 | 2010-08-25T20:17:24.127 | null | null | 930 | null |
2118 | 2 | null | 2116 | 4 | null | You can look at the solution given on the question ["Statistical models cheat sheet"](https://stats.stackexchange.com/questions/1252/statistical-models-cheat-sheet)
| null | CC BY-SA 2.5 | null | 2010-08-25T21:24:22.967 | 2010-08-25T21:24:22.967 | 2017-04-13T12:44:25.243 | -1 | 253 | null |
2119 | 1 | 2123 | null | 3 | 920 | Some guys told me that it's appropriate to use Wald-Wolfowitz Runs Test as a normality test (like Shapiro-Wilk's or Kolmogorov-Smirnov...). Do you think this is good way to test normality assumptions?
| Wald-Wolfowitz Runs Test for Normality Assumptions Testing | CC BY-SA 2.5 | null | 2010-08-25T21:56:01.473 | 2010-08-25T22:54:03.987 | null | null | 1356 | [
"normality-assumption",
"hypothesis-testing",
"nonparametric"
] |
2120 | 2 | null | 1912 | 1 | null | Let me take a stab at this...
If I understand the original sentiment and subsequent response, I think what Gary King is getting at is that noticing a scale invariance effect on your data is a very gross understanding of the phenomena of whats going on. While this 'birds eye view' of whatever phenomena you are observin... | null | CC BY-SA 2.5 | null | 2010-08-25T21:58:52.960 | 2010-08-25T21:58:52.960 | null | null | null | null |
2121 | 1 | null | null | 24 | 15303 | I am using ridge regression on highly multicollinear data. Using OLS I get large standard errors on the coefficients due to the multicollinearity. I know ridge regression is a way to deal with this problem, but in all the implementations of ridge regression that I've looked at, there are no standard errors reported for... | How can I estimate coefficient standard errors when using ridge regression? | CC BY-SA 2.5 | null | 2010-08-25T22:34:45.943 | 2016-09-14T03:04:07.357 | null | null | 1090 | [
"standard-error",
"ridge-regression"
] |
2123 | 2 | null | 2119 | 6 | null | This is not a great idea. The Wald-Wolfowitz test can detect certain departures from being independently distributed (it detects whether a series of observations is 'too bunchy' or 'too jittery' with respect to the distribution of positive and negative values), which have nothing to do with normality. It is not sensit... | null | CC BY-SA 2.5 | null | 2010-08-25T22:54:03.987 | 2010-08-25T22:54:03.987 | null | null | 795 | null |
2124 | 2 | null | 2007 | 2 | null | [RapidMiner](http://rapid-i.com/content/view/181/190/lang,en/) (Java) [open source]
| null | CC BY-SA 2.5 | null | 2010-08-25T22:55:22.173 | 2010-08-25T22:55:22.173 | null | null | 170 | null |
2125 | 1 | 2128 | null | 103 | 281806 | In particular, I am referring to the Pearson product-moment correlation coefficient.
| What's the difference between correlation and simple linear regression? | CC BY-SA 3.0 | null | 2010-08-25T23:53:00.417 | 2022-11-16T01:15:25.060 | 2013-08-01T17:07:24.153 | 7290 | 74 | [
"correlation",
"regression"
] |
2126 | 2 | null | 2121 | 10 | null | Assuming that the data generating process follows the standard assumptions behind OLS the standard errors for ridge regression is given by:
$ \sigma^2 (A^T A + \Gamma^T \Gamma)^{-1} A^T A (A^T A + \Gamma^T \Gamma)^{-1}$
The notation above follows the wiki notation for [ridge regression](http://en.wikipedia.org/wiki/T... | null | CC BY-SA 2.5 | null | 2010-08-26T00:05:08.597 | 2010-08-26T00:30:18.583 | 2010-08-26T00:30:18.583 | null | null | null |
2127 | 2 | null | 2111 | 4 | null | This is a huge topic. As previously mentioned, Hastie, Tibshirani and Friedman give a good intro in Ch3 of Elements of Statistical Learning.
A few points.
1) What do you mean by "best" or "optimal"? What is best in one sense may not be best in another. Two common criteria are predictive accuracy (predicting the outcome... | null | CC BY-SA 2.5 | null | 2010-08-26T00:56:53.163 | 2010-08-26T00:56:53.163 | null | null | 521 | null |
2128 | 2 | null | 2125 | 121 | null | What's the difference between the correlation between $X$ and $Y$ and a linear regression predicting $Y$ from $X$?
First, some similarities:
- the standardised regression coefficient is the same as Pearson's correlation coefficient
- The square of Pearson's correlation coefficient is the same as the $R^2$ in simple ... | null | CC BY-SA 4.0 | null | 2010-08-26T02:48:15.233 | 2022-11-16T01:15:25.060 | 2022-11-16T01:15:25.060 | 183 | 183 | null |
2129 | 2 | null | 2125 | 36 | null | Here is an answer I [posted on the graphpad.com website](http://www.graphpad.com/faq/viewfaq.cfm?faq=1141):
Correlation and linear regression are not the same. Consider these differences:
- Correlation quantifies the degree to which two variables are related. Correlation does not fit a line through the data.
- With ... | null | CC BY-SA 2.5 | null | 2010-08-26T03:21:45.833 | 2010-08-26T03:21:45.833 | null | null | 25 | null |
2130 | 1 | 2139 | null | 3 | 342 | Consider the following exponential expansion form:
$$ \exp\left[\sum_{k=1}^\infty \gamma_k x^k\right] = \sum_{j=0}^\infty\delta_j x^j $$
where $\gamma_k$'s are known, and $\delta_0=1$,
$$\delta_{j+1} = \frac{1}{j+1}\sum_{i=1}^{j+1}i\gamma_i\delta_{j+1-i}$$
Does anyone know how to prove it?
| How to prove the exponential expansion recursive form? | CC BY-SA 3.0 | null | 2010-08-26T04:33:09.090 | 2013-01-18T17:47:57.020 | 2013-01-18T17:47:57.020 | 2970 | 1043 | [
"mathematical-statistics"
] |
2131 | 1 | 2133 | null | 10 | 8303 | for eg. if I have a class variable credit scoring with two classes good and bad, where #(good) = 700 and #(bad)= 300. I do not want to shorten my data. which technique I should use? I was using SVM but it is giving all bad as good in predictions.
| For a classification problem if class variable has unequal distribution which technique we should use? | CC BY-SA 2.5 | null | 2010-08-26T04:45:31.653 | 2022-04-20T09:45:31.183 | 2010-08-26T07:02:24.003 | null | 861 | [
"classification",
"binary-data"
] |
2132 | 2 | null | 2125 | 27 | null | In the single predictor case of linear regression, the standardized slope has the same value as the correlation coefficient. The advantage of the linear regression is that the relationship can be described in such a way that you can predict (based on the relationship between the two variables) the score on the predict... | null | CC BY-SA 2.5 | null | 2010-08-26T06:37:23.230 | 2010-08-26T06:37:23.230 | null | null | 196 | null |
2133 | 2 | null | 2131 | 7 | null | Your class sample sizes do not seem so unbalanced since you have 30% of observations in your minority class. Logistic regression should be well performing in your case. Depending on the number of predictors that enter your model, you may consider some kind of penalization for parameters estimation, like ridge (L2) or l... | null | CC BY-SA 4.0 | null | 2010-08-26T07:04:58.167 | 2022-04-20T09:45:31.183 | 2022-04-20T09:45:31.183 | 79696 | 930 | null |
2134 | 1 | null | null | 15 | 15980 | I'm looking for an `R` package for estimating the coefficients of logit models with individual fixed-effect (individual intercept) using Chamberlain's 1980 estimator. It is often known as Chamberlain's fixed-effect logit estimator.
It's a classic estimator when dealing with binary outcome panel data (at least in econ... | R package for fixed-effect logistic regression | CC BY-SA 3.0 | null | 2010-08-26T08:03:41.620 | 2015-04-26T12:49:09.817 | 2013-09-03T11:58:44.917 | 805 | 1091 | [
"r",
"logistic",
"panel-data",
"clogit"
] |
2135 | 2 | null | 2131 | 10 | null | A popular approach towards solving class imbalance problems is to bias the classifier so that it pays more attention to the positive instances. This can be done, for instance, by increasing the penalty associated with misclassifying the positive class relative to the negative class. Another approach is to preprocess th... | null | CC BY-SA 2.5 | null | 2010-08-26T08:45:35.657 | 2010-08-26T08:45:35.657 | null | null | 339 | null |
2136 | 2 | null | 2134 | 13 | null | Conditional logistic regression (I assume that this is what you refered to when talking about Chamberlain's estimator) is available through `clogit()` in the [survival](http://cran.r-project.org/web/packages/survival/index.html) package. I also found this page which contains R code to estimate [conditional logit parame... | null | CC BY-SA 2.5 | null | 2010-08-26T08:59:08.873 | 2010-08-26T08:59:08.873 | null | null | 930 | null |
2137 | 2 | null | 2121 | 27 | null | I think boostrap would the best option to obtain robust SEs. This was done in some applied work using shrinkage methods, e.g. [Analysis of North American Rheumatoid Arthritis Consortium data using a penalized logistic regression approach](http://www.biomedcentral.com/1753-6561/3/S7/S61) (BMC Proceedings 2009).
There is... | null | CC BY-SA 2.5 | null | 2010-08-26T09:36:21.003 | 2010-11-01T16:24:39.703 | 2010-11-01T16:24:39.703 | 930 | 930 | null |
2138 | 2 | null | 2067 | 9 | null | As a consequence of the inspiring answers and discussion to my question I constructed the following plots that do not rely on any model based parameters, but present the underlying data.
The reasons are that independent of whatever kind of standard-error I may choose, the standard-error is a model based parameter. So... | null | CC BY-SA 2.5 | null | 2010-08-26T10:15:37.873 | 2010-12-09T12:30:33.363 | 2010-12-09T12:30:33.363 | 442 | 442 | null |
2139 | 2 | null | 2130 | 2 | null | A brute-force approach would use a [Taylor series](http://en.wikipedia.org/wiki/Taylor_series) expansion of $\exp(\cdot)$ at 0 and group terms appropriately to demonstrate the relationship. I have not attempted to do so myself but an inspection of the first few terms does indicate that the proposed relationship holds. ... | null | CC BY-SA 3.0 | null | 2010-08-26T10:36:17.803 | 2013-01-17T13:59:57.010 | 2013-01-17T13:59:57.010 | 17230 | null | null |
2140 | 1 | 2141 | null | 4 | 1973 | I'm investgating the repeatability of an experimental process (actually measuring lift in a wind tunnel), which can be measured using a single number, Q. However, I only have limited access to the tunnel so the number of experiments that I can run is restricted.
If I have 5 experiments with the tunnel in one configurat... | How do I compare the repeatability of two sets of experiments with a low number of samples? | CC BY-SA 2.5 | null | 2010-08-26T11:42:13.020 | 2010-08-29T14:41:44.697 | 2010-08-26T13:07:19.183 | 279 | 1094 | [
"hypothesis-testing",
"variability"
] |
2141 | 2 | null | 2140 | 4 | null | Look up the two-sample F-test for variances. With equal sample sizes Q, this test calculates the ratio of the two variances, and then compares the value to the F-distribution with (Q-1, Q-1) degrees of freedom. A main assumption is normality, but that might be OK for random measurement error like this.
With your value... | null | CC BY-SA 2.5 | null | 2010-08-26T13:06:34.753 | 2010-08-26T13:06:34.753 | null | null | 279 | null |
2142 | 1 | null | null | 9 | 12146 | When performing linear regression, it is often useful to do a transformation such as log-transformation for the dependent variable to achieve better normal distribution conformation. Often it is also useful to inspect beta's from the regression to better assess the effect size/real relevance of the results.
This raises... | Linear regression effect sizes when using transformed variables | CC BY-SA 2.5 | null | 2010-08-26T13:07:47.100 | 2010-11-02T17:21:52.077 | null | null | null | [
"regression",
"data-transformation",
"effect-size"
] |
2143 | 2 | null | 2142 | 2 | null | SHORT ANSWER: Absolutely correct, the back transformation of the beta value is meaningless. However, you can report the non-linearity as something like. "If you weigh 100kg then eating two pieces of cake a day will increase your weight by approximately 2kg in one week. However, if you weigh 200kg your weight would i... | null | CC BY-SA 2.5 | null | 2010-08-26T13:30:32.093 | 2010-08-26T13:44:01.287 | 2010-08-26T13:44:01.287 | 601 | 601 | null |
2144 | 2 | null | 2140 | 2 | null | If you want a non-parametric approach that doesn't assume normality, you could obtain distributions of SDs for each configuration using bootstrapping, then compare these distributions. Comments below the answer to [this question](https://stats.stackexchange.com/questions/1169/ci-for-a-difference-based-on-independent-ci... | null | CC BY-SA 2.5 | null | 2010-08-26T13:49:10.053 | 2010-08-26T13:56:57.967 | 2017-04-13T12:44:23.203 | -1 | 364 | null |
2145 | 2 | null | 2130 | 6 | null | Srikant asks for a "more elegant approach." Perhaps the following will respond to this challenge.
Let the argument of the exponential be $f(x)$ (so its power series coefficients are the gammas) and let the right hand side be $g(x)$ (with deltas as its coefficients), so that by definition
$$g(x) = \exp(f(x)).$$
Differe... | null | CC BY-SA 3.0 | null | 2010-08-26T14:43:02.220 | 2013-01-17T16:10:06.383 | 2013-01-17T16:10:06.383 | 919 | 919 | null |
2146 | 1 | 49881 | null | 1 | 144 | I have to compare pairs of audio strems as 1d time series. Looking at the aligned trajectories I need to either cluster them together or assume they arise from independent generators.
I remember hearing of Dirichlet processes being applied. Could try to defined a hidden Markov model on the structure, not sure yet how... | For data similar to audio, how to determine if there are 1 or 2 categories? | CC BY-SA 3.0 | null | 2010-08-26T17:26:23.483 | 2013-02-12T23:00:11.953 | 2013-02-12T22:56:08.027 | 196 | 1098 | [
"machine-learning",
"time-series",
"clustering"
] |
2147 | 2 | null | 1142 | 32 | null | A good solution will have several ingredients, including:
- Use a resistant, moving window smooth to remove nonstationarity.
- Re-express the original data so that the residuals with respect to the smooth are approximately symmetrically distributed. Given the nature of your data, it's likely that their square roots ... | null | CC BY-SA 2.5 | null | 2010-08-26T18:02:43.510 | 2010-11-03T00:21:10.310 | 2010-11-03T00:21:10.310 | 919 | 919 | null |
2148 | 2 | null | 2142 | 5 | null | I would suggest that transformations aren't important to get a normal distribution for your errors. Normality isn't a necessary assumption. If you have "enough" data, the central limit theorem kicks in and your standard estimates become asymptotically normal. Alternatively, you can use bootstrapping as a non-parametric... | null | CC BY-SA 2.5 | null | 2010-08-26T20:43:29.787 | 2010-08-26T20:43:29.787 | null | null | 401 | null |
2149 | 1 | 2153 | null | 59 | 43521 | A [particle filter](http://en.wikipedia.org/wiki/Particle_filter) and [Kalman filter](http://en.wikipedia.org/wiki/Kalman_filter) are both [recursive Bayesian estimators](http://en.wikipedia.org/wiki/Recursive_Bayesian_estimation). I often encounter Kalman filters in my field, but very rarely see the usage of a partic... | What is the difference between a particle filter (sequential Monte Carlo) and a Kalman filter? | CC BY-SA 2.5 | null | 2010-08-26T21:00:11.910 | 2020-09-06T16:19:25.457 | null | null | 5 | [
"bayesian",
"particle-filter",
"kalman-filter"
] |
2150 | 2 | null | 2032 | 21 | null | There's no need to call it Predictive Analytics :) It already has two names: statistics, and data mining.
Beginner Stats Book: Statistics in Plain English
Advanced Stats Book: Multivariate Analysis, by Hair
Data Mining Book: I still haven't found a great one, but Data Mining by Witten is okay.
Don't get too confused... | null | CC BY-SA 2.5 | null | 2010-08-26T21:24:56.327 | 2010-08-26T21:24:56.327 | null | null | 74 | null |
2151 | 1 | null | null | 97 | 163601 | In other words, instead of having a two class problem I am dealing with 4 classes and still would like to assess performance using AUC.
| How to plot ROC curves in multiclass classification? | CC BY-SA 2.5 | null | 2010-08-27T01:56:42.663 | 2021-04-12T16:53:22.913 | 2010-08-27T02:41:15.557 | 159 | null | [
"classification",
"roc"
] |
2152 | 2 | null | 2151 | 5 | null | While the math is beyond me this general review article has some references you will likely be interested in, and has a brief description of multi-class ROC graphs.
An introduction to ROC analysis by Tom Fawcett
Pattern Recognition Letters
[Volume 27, Issue 8, June 2006, Pages 861-874](http://dx.doi.org/10.1016/j.patre... | null | CC BY-SA 2.5 | null | 2010-08-27T02:58:26.190 | 2010-08-27T13:22:46.397 | 2010-08-27T13:22:46.397 | 1036 | 1036 | null |
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