tttjjj/elt_bench huggingface dataset:
SPSS still computes standard errors (based on the assumption that the series are not cross correlated and one of the series is white noise) and shows corresponding confidence intervals.
Welcome to the site! Generally speaking, GLM assumes independent observations and thus cannot handle repeated measures. GLMM can provide subject (or site/block/season in your example) specific estimates, but the marginal model (e.g. generalized estimating equations) is another solution. The clarification between subject specific and population estimates can be found [here](http://stats.stackexchange.com/questions/59137/subject-specific-vs-population-average-predictions/68531#68531).
+1 for interesting real case ! can you help us to understand what is blood test ? (how do you calculate it)
AR(1) and ARIMA(2,0,1)
It may be best to move this to [chat](http://chat.stackexchange.com/rooms/18/ten-fold). That *is not* what SPSS labels the explanatory variable! That is the label given to the *predicted* value from a linear regression model.
@Miroslav Sabo, could you possibly help?
@chl  I asked the question, although I almost had the result: I was not aware at the time I asked, sorry. Having $\\nu=0$ gives the result since there is a special link between $\\alpha$ and $\\mu$. There were more details in my first edit about this point (I edited as I did not want to give the whole solution away, with respect to the professor who gave the homework, due for today).
Thanks for your reply :-) I think this is indeed what I am looking for, although I am unsure about whether I need GLM or not. The more I read about it, the more I doubt between using GLM or OLS. Please refer to the output [here](http://i.imgur.com/mherkpy.png) and tell me your thoughts on this issue based on the output. Your help is much appreciated.
+1 But there's a more benign explanation for the reduction in correlation with the logs: please see http://stats.stackexchange.com/questions/41734/attainable-correlations-for-lognormal-random-variables. No "strangeness" is needed in the distributions, only a small difference in standard deviations.
How do you _get_ two normal _distributions_? Does each measurement consist of many observations (samples) from which you _estimate_ the values of $m_1$ and $s_1^2$ as the sample mean and sample variance, or do you observe two numbers which you are modeling as single samples from two _independent_ normal random variables with known means and variances? Note that @Max's comment implicitly assumes independence of the two samples.
@Hemmo Yikes, now I feel like I should actually contribute something.  You're right that nothing is actually wrong.  The short answer (which I was hoping to write up but didn't find the time to), is that the mean will be zero iff the likelihood surface is Gaussian.  Informally, we can "prove" this by noting that Gaussian errors * Gaussian random effects leads to another Gaussian. When you have a glmm with a non-Gaussian error function (e.g. Poisson in your case), then the likelihood surface can become non-Gaussian, and all bets are off.  Hope this helps.
+1 for mentioning dependencies. These arise for reasons other than interpersonal relationships, such as weather and travel conditions. Many of them induce positive correlations--which widen the range of uncertainty. If the estimates will be used to provide logistics (meals, seats, and so on), assessing the variation accurately is valuable. Although in a wedding application one can't do much more than make an educated guess, having a qualitative understanding of these statistical phenomena can lead to better guesses.
Oh I see what you mean now. The short answer is, you can't, at least not without reengineering the SVM to understand the notion of recency. An SVM is defined in terms of x-y pairs, not ordered x-y pairs, and so the objective function it minimizes is a function of said pairs. OTOH, you could run several separate SVMs, one with the past month, one with the past year, and one with the past two years -- and then do a weighted average of the responses you get back based on your personal preference for recent vs. old data (e.g. 0.7 * past month + 0.2 * past year + 0.1 * past 2 years).
*systematic sampling* and *stratified sampling* are examples for methods distinct from *simple random sampling*.
Technically, it doesn't have to actually *be* homework, just equivalent to homework. Please don't take offense, we get a lot of people posting their homework here hoping someone will do it for them. So it's a sensitive topic.
So a) is there something in common between item 411 in category 15 and item 411 in category 35? and b) is there anything *different* between the two?  In the end, is it item 411 that is important rather than its category?
your first (auto.arima) model doesn't have an intercept... try using a-mean(a) and b-mean(b) to eliminate the need for one. You'll get the same coefficient estimate that lm gives if you do.
If your prior for some parameter is a constant value (i.e. if the variance is zero), you're saying there's *no* chance it's anything else. In that case, won't the posterior simply be the same fixed value?
Having used SAS for 23 years and S-Plus/R for 22 years I can say that a highly experienced SAS programmer can be highly productive, but that an experienced R programmer can be easily three times as productive.
Awesome. 
It's just that as a mathematician, this (-5,5) seemed weird.
I get it now with the alphas.

Thanks a lot.
Any idea about the graph ? I tried running the function for some N's with a for loop and a dotplot from lattice, but it didn't work.
@StasK I did see a reference to Pinheiro and Bates earlier, but for some reason I can't find it now ! I have seen the Technical Note regarding prediction of random effects; that it uses "standard theory of maximum likelihood" and the given result that the asymptotic variance matrix for re's being the negative inverse of the Hessian. but to be honest this didn't really help me ! [maybe due to my lack of understanding]
Hi! By analysis of raw data I mean the analysis without any aggregation (on subject level or on stimulus level). Perhaps, the title of the question is misleading, as it is this kind of analysis (without) aggregation that I meant by "item-based". I'll rename it.
@suncoolsu asymptotic unbiasedness is very different from the usual sense of "unbiased". Any reasonable estimate should be asymptotically unbiased.  One more note: shouldn't the statement about admissible estimators be the other way around? ie every admissible estimator is generalized Bayes.
If I'm not mistaking, if you don't provide all the variables that were used to fit a model, predict will throw an error?
Chi-squared calculations in the usual form is for counts. If you scale all the counts down to percentages, you scale the chi-square down as well. (It's possible to work with percentages if you know the totals by which every value was scaled, but it's easier to do it with the counts.) ... there are many elementary tutorials on the various forms of chi-square test (goodness of fit, homogeneity / independence, and a number of others) that might be used on such data.
Can you post your R code and a [reproducible example](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example), please? If the response variable has 8 levels, don't you think that a multinomial or ordinal logistic regression might do the job as well?
@DrWho: Simply right click on http://www.autobox.com/AFSUniversity/afsuFrameset.htm and then select "open link in new window" and then left click on  "Entertainment" which will bring up the text. Higlight the text and copy to the clipboard and then paste to a word doc and you are good to go. I will look at your revised data and try to answer your "new question"
Thank you, comrade!
Also closely related: http://stats.stackexchange.com/questions/4220/probability-distribution-value-exceeding-1-is-ok and http://stats.stackexchange.com/questions/14483/intuitive-explanation-for-density-of-transformed-variable.
As long as you make it clear what you are reporting, you're OK, but the usual method of reporting means and sds, as far as I've seen, is mean = 1.23 (sd = 0.52) or something similar. 

The $\\pm$ sign used the way you are proposing risks confusing people into thinking it is a 95% CI, which it isn't.
Thanks. I have kept the empirical variance for $\\sigma^2$, but I have managed to quicken the convergence by using another covariance matrix for the proposal.
It was such a minor thing, I figured I'd just go ahead and make the edits. The spelling is unusual (at least in English!), especially compared to the more common variant. :-)
what about going to google scholar and do some search, including relevant author names?   Your thesis advisor should have thaught you how to do literature searches.
I rephrased my question. Or should I post modifications elsewhere on the page?
M.:> it should be okay: i only need Q (i.e. qmult(eye(p)))
Wouldn't these be *counts* of floods during 1000 years, and not the *events* (or at least their times) themselves? What you need to make progress with this question is the relationship between the Poisson distribution for counts and the Exponential waiting time between them.
`di invchi2(1,0.95)`. Type `help density_functions` for more information.
I don't see any recommendation on that math thread concerning 30, Cassandra.  But what's so frustrating?  All I have said is you should *simplify* your life by not breaking your observations into groups of 30 and doing lots of tests.  Just test all the observations at once--and using a chi-squared test is fine for that.  (Intuitively you haven't any reasonable hope of detecting small changes in probabilities with only 30 rolls: *all* observed relative frequencies will be multiples of $1/30 = 3\\frac{1}{3}$%, which is poor precision.)
This is a classic survival analysis situation. Do you have any censoring? That is, do you have any beans that still hadn't germinated by the end of the study period?
(+1) and thanks for the reproducible example.
There is only one package which does panel data econometrics in R and that is **plm**. And not surprisingly it has a function which does IPS test and few other panel unit root tests (**purtest**). By the way practically all of classical panel data econometrics assume cross-section independence.
@Germaniawerks, thank you for answering. Yes, I meant exponential distribution. I think it's wrong to use the K-S test over all the projects, but I wanted to know which curve was close to the frequency distribution of the X features.
I see, so it is not required it is just a simplification? If you phrase your comment as an answer I can mark it as such.
It's true that you use it, but if you do it's typically NOT noninformative, since then you could also use a more typical/easier noninformative prior like gamma or the uniform. The half-t makes more sense as a weakly informative prior, which has a distinct tendency but wide enough tails to let itself be overwhelmed by the data in the face of strong evidence.
As you yourself point out, we need _enough_ observations. Naturally, what is _enough_ depends on how long the long range is...
Do you have any reference or any measure on true votes? How are you measuring candidate deservedness
@ricardo That sounds simple to explain.  Suppose Z is a linear function of X1, X2, and X3 and Z is a useful predictor of the model response.  Then in combination X1, X2, and X3 are significant in explaining y but none of them by themselves would be.
Is T dichotomous, polytomous, or continuous? Ratios of means (geometric means) can be calculated using log transformations.
@whuber: The values are a set of distance measurements of an object's outline from its centroid, normalized to [0, 1]. The paper uses binning of these distances into $2^J$ bins, finding the optimal $J$ by minimizing the sum of the quantization error (from binning) plus the pdf of the histogram. To the best of my understanding.
yeah sure, it's good for everything. In fact, it's not the matter of bein good or bad, it's just right way for doing this.
Hi Laura, welcome to the site!  I don't think LDA is the right approach here; LDA wants continuous predictors and a categorical outcome. If I understand correctly, you want to know which species differ in abundance when the treatment is applied - which means you have categorical predictors and continuous outcomes! To me, that suggests something more like t-tests, at the most basic, or multilevel models/generalized estimating equations, if you want to get fancier and have explicit estimates of things like site and year effects.
@GaBorgulya Usually, [SEM](http://www.ncme.org/pubs/items/16.pdf) is computed in a different way; contrary to SD or SE, it is supposed to account for scores reliability, specific to the measurement instrument.
@Matteo De Felice: If you're having trouble implementing time-series cross-validation, this blog post might help: http://robjhyndman.com/researchtips/tscvexample/
I do not want (nor do I intend) to chide, but I would like to *encourage* votes on questions that strike users as interesting enough to answer. Currently this question has five answers but no votes. Voting helps convey interest and helps other users sort content. Cheers.
By 'clusters' do you mean possible values of the categorical variable, e.g. chalk, cheese, ham, egg, chips, or peas?
Sounds like something well-suited to `R`, which meets your open source criterion... and you can get excellent assistance from the clever folk at http://stackoverflow.com/tags/r/info
Hello and many thanks for your reply. Regarding the transition matrix it simply is the "number of transitions from one state to another" / "number of all possible transitions from current state". You are right all rows should add to 1, I have corrected that, 3 is let's say a terminal state so once reaching it the system remains in that state. The observations are dummy observations of whatever, let's say boxing moves 1:right punch, 2: left punch, 3:arms guarding head... I will have a look on the link you provided. Thanks again.
@Shadi Following my latest comment, I asked for closing this question so that you can reformulate a new one by adding precision on your design, especially the fact that you actually have 6 similarity matrices instead of 6 series of measurement. This way, others may provide useful insights into this question. You can still link to this question, but I really feel it call for a new thread with your added clarifications so that everyone can contribute.
Related thread: [Best practice when analysing pre-post treatment-control designs](http://stats.stackexchange.com/q/3466/930), and probably also of interest: [Which t-test to use for a two-group pre- post-test design?](http://stats.stackexchange.com/q/30708/930)
@Cruncher Universities also have a high proportion of equal-age relationships. But I won't dispute your numbers; I have no good basis for my guess.
Thankyou, that's fantastic. I wasn't sure how it related to structural equation models which I am more familiar with. I own that text and will retrieve it from the person I lent it to.
Though you could use `mean(range(x))`
Thanks for your contribution.  It leaves some questions hanging, though: (a) In what sense do you see this as a multi-objective optimization problem?  (b) What do you specifically propose as the "energy" to minimize?  (c) Because there are $n!$ possible pairings, an exhaustive search will be prohibitively long once $n$ is much larger than $13$ or so (at which point there are over 6 billion pairings to examine...).
`go out on the street` violates the OP's assumption `Something... I can do at home`
Thanks for taking care of filling this thread. (I totally forgot about it, despite your ping :-)
I would agree with you on that.
"*But you may get away here for practical reasons with cross validating again if you just step once towards a less complex model. If you do that, I'd recommend you make the reader aware that another validation should have been done, but that was impossible for practical reasons.*. While true, this is only useful if the other party disposes of statisticians. In my experience, often the actual users of predictive models (for example clinical physicians) do not want this information as they don't know what to do with it. Providing too much info is sometimes considered to be communication noise.
It seems like it would be better to envision the centreline of a pipe as piecewise linear (perhaps joined by sections that are arcs of circles). You could try writing an expected temperature in terms of distance from the centerline and construct a nonlinear least squares problem from that.
Do you mean `For sick patients, the test is NOT very accurate.` you forget the NOT?
In anything but quite small samples, the "quickie approach" based on the ARE works surprisingly well for normal data, if you adjust to use the actual significance level you will have with the signed rank test (rather than the nominal). [Here](http://i.stack.imgur.com/ojFcS.png) is signed rank power (via simulation from normal samples) at n=16 (points), and the implied power curve for a t-test with variance adjusted to compensate for the ARE (red curve), using the attainable significance level. It's not so useful on non-normal data of course.
The function `attibutes(loglik(obj))` is working fine. The reason why it gives a number larger in 1 unit to the value is because you have to estimate the mean value of the model without any covariate. The mean of the model, let's say. So it is working perfect.
Thanks Douglas!  Thanks for your remark regarding the use integer-valued statistics.  I'll throw out the first ranking, but the second ranking algorithm I came up with, $\\displaystyle\\prod_{i=1}^{n-1}P(O_i > O_{i+1}) \\times 2^{n-1}$, can be refactored as $\\displaystyle\\prod_{i=1}^{n-1} 2 \\times P(O_i > O_{i+1})$.  This can be reduced to counting $i$ such that $O_i > O_{i+1}$ as per your suggestion.  I'm still thinking of how to make use of your inversion insight to produce a ranking.
It's a good question.  There is, however, no objectively supportable basis for your *ad hoc* approach.  That doesn't mean it will perform poorly--but it strongly suggests there are better procedures.  To find them, it would help if you could edit this question to explain what kinds of errors you are attempting to identify, including how large they might be, how many of them might occur, and what the consequences are of (a) failing to identify some of the errors and (b) flagging correct data as errors.
check out Timmermans, T., Van Mechelen, I., & Kuppens, P. (2010). The relationship between individual differences in intraindividual variability in core affect and interpersonal behaviour. European Journal of Personality, 24, 623.http://ppw.kuleuven.be/okp/_pdf/Timmermans2010TRBID.pdf
State effects are implicitly controlled for by using fixed effects.  If your time dummies are auto-dropped, its probably because they are indeed perfect linear combinations of other things you've got.  If so, you're good.
Please, don't [cross-post](http://stackoverflow.com/questions/7280845/java-support-for-pmml).
You need to make your subject variable a factor.
I ended up using a free Wikispaces wiki as my current experimental lab notebook substitute.
You could consider adding other relevant tags to your question.
@Syler, Yes, provided that the chi^2 test is appropriate in your instance.  Have you tried reading [this](http://en.wikipedia.org/wiki/P-value)?  You will often find more complete answers with worked examples by reading wikipedia than by asking on SO.  People will also be able to give you better answers (and feel more inclined to help you) if you phrase your question in the form: "I was reading [link] but I don't understand [quote]..."
Thank you for reference. The problem is my measure theory skills are rusty. For example I cannot find a way to prove formula (12) from the paper you referenced. (But I've learned much from it nonetheless). Similar questions happen more often than not and when I'm hitting the wall there is no one no ask...
(1) If your fitting algorithm isn't converging that's likely your main problem. (2) For a GARCH model, or any other, to perform well it must make good predictions. Statistically significant correlation in the standardized squared residuals may be tolerable if it's tiny. If you want to improve the model look for signs of mis-specification & consider more parameters for the GARCH part.
I'm pretty sure some normalizing constants are missing from the integrals: you need to divide them by $\\sqrt{2\\pi}s$.
You can make your equations readable by using TeX: enclose expressions in "$\\$$", as in "$\\$$ \\hat{b} Y_{1.2} $\\$$" to display $\\hat{b} Y_{1.2}$.
@cardinal Because actually I am not after sampling now(I will use it later when I need Monte Carlo methods). Right now I am dealing with the case of parametrization of a $n-1$-sphere to be able to assign probability to points on it. A uniform sampling method  based on this parametrisation will provide me with one way to do so, right?
In addition to @whuber's point, the marginal distribution of the response is not really of interest, but rather the conditional distribution / the distribution of the residuals. On another note, are all Y values integers / counts?
@gung You mean that the given elements will be accepted as normal? But we cannot reject the hypothesis that an element is abnormal either in this case I guess...
Yeah, that works. If I'm over 30 points in the dataset I'm doing something wrong.
"*First it estimates a continuous factor (clay) with only one degree of freedom which seems strange*" --- you should probably familiarize yourself with ordinary (fixed effects) regression first; this notion should be familiar to you from there. The fact that it isn't suggests that you lack some important background (either in understanding why continuous variables will have 1df or in understanding how to fit a model in a continuous variable that has more df)
+1 (Sorry about the belated vote): nice illustrations.
I must be confused by the terminology, sorry.  When you say error is aggregated out-of-sample do you mean of the whole sample, including those points which are not in the training set?  I'd think of that as whole-of-sample, as you still don't have any points from outside your sample (be definition...).
@ACD, no, i am trying to implement the second test, namely test for Homogenous Causality (second of 3). The whole procedure is  described in Law, Lee and Ismail (2013), but i have no idea how to estimate this restricted equation that accounts for the null hypothesis of the HC test.
Hi Rob, Thanks for your answer. Do you have in mind any material on the web or somewhere else on the method used to calculate the standard error (The standard errors associated with the smoothing weights are calculated from the Hessian matrix of the sum of squared, one-step-ahead prediction errors with respect to the smoothing weights used in the optimization process)?
Much appreciated, and sorry to be dense ni6go... would I be right thinking then that if the states were not positive recurrent, I would have been unable to find {$\\pi$} that solved both conditions (above)?
I edited difference in $R^2$ to be 30%, not 30. Presume that is what you meant.
Sorry now that you have reformulated your question, my answer is not really appropriate
Great and simple answer. You have an appreciable knowledge at an young age.
I don't know too much about survival analysis. If you look on this site or on Google for "survival analysis assumptions", that could get you in the right direction.
By poor job I mean, OLS did not effectively capture the bias and thusly I could not subtract it out. By othogonal least squares I mean http://en.wikipedia.org/wiki/Total_least_squares