tttjjj/elt_bench huggingface dataset:
Welcome to the site! A possible solution is to generate a third variable (say `z`) based on a logical expression like `if(x7==0 & x8==1) then z=1`, where `severe` denoted as 1. I am not sure about how to do this in `SPSS`, but it is easy to do so in `Excel` and other statistical software. If you set a variable as ordinal or nominal, `SPSS` would not provide the mean but the frequency.
What is multivariate statistics?  Seriously, this is such a vast subject and covers so much ground that it's impossible to know what you're looking for and certainly it would be impossible for any textbook to claim a "detailed" coverage of the subject!
I meant, I just saw your answer
The difficulty with Bayesian methods when the prior is not nice (like a conjugate prior) is that the normalization constant is an integral that can be difficult to evaluate.  This is one of the reason MCMC methods are so popular.
You will also want to read: [Removal of statistically significant intercept intercept term boost $R^2$ in linear model](http://stats.stackexchange.com/q/26176/7290).
+100 if I could. That example was amazing.
I'm interested in what your goal is here. Are you trying to match recipients to donors? Or determine the likelihood that two individuals will be matched given information on each of them?

I'm going to guess that you don't have any information on how donor attributes are correlated, if you only have summary statistics. Do you have any other information that might be useful here?
I'm having trouble wrapping my head around your design... At what level was the randomization done? At the standtype*treatment level? How many standtype*treatment level 'units' are there? 4×3×6=72? And then 72×6=432 plots?
Why does it matter what the distribution is?
@user3790338 I recommend reading the original articles by Wilcoxon and by Mann and Whitney, also.
+1, good answer, would you mind adding something like "Indeed, [it is possible that] none of the points may lie... ". Also, it might be worth discussing that the notion of how far the points are from the line is also relative to how spread out the X's are.
...and why doesn't it have a [crop or pasture growing in it](https://www.google.com/search?q=field+definition), either?
thanks. Can't afford commercial software. Working in R. So some existing R code will be nice. :)
Make all the numbers 0.5; it seems to fulfill all of the conditions you state. (It might sound like I am being facetious, but there's an important point being made; there are clearly additional conditions and expectations that should be explicit.)
Thanks Placidia.. great help.
Whops. I made a mistake in the writing, still for the percentage change I have to take the exp(), since the dependent variable is in logs, the independent is not. `exp(.2566441)= 1.292585 = 29% increase`
@gung I'm sorry probably my knowledge is worst than i thought. I meant participants to this experiment are asked to choose a range, like [0,20] minutes or [60,100] and not a specific value, like 35 minutes. What i meant is that I do not have each participant exact value, just the range in which their waiting time was.
what you call the mad ('maximum absolute deviation') is called the Chebychev norm (or sup norm).
Confidence interval ... for what quantity?
I wrote up a fairly comprehensive overview / guide to using simulations for power analyses here: [Simulation of logistic regression power analysis - designed experiments](http://stats.stackexchange.com/a/35994/7290), but I didn't do a very good job w/ this part. @GregSnow's [answer](http://stats.stackexchange.com/a/36040/7290) does a better job.
Do you mean consistent over time?  I don't follow your method for the individual series, but if it is sound (you might want a separate question on that) why not just use the same method with the average of the two series, scaled so they are both on the same scale (I can't think of a better measure of "all over performance")?
+1 for the bar chart and frequency table. I would also add cumulated frequencies to the table. They will tell how many people have spent less than 30 minutes, less than 45 minutes, ...
There is a lot of controversy regarding their approaches.  An over-simplified way to summarize these issues is that if you want to learn about patterns and make new discoveries that need later validation, exploratory analysis is appropriate.  If you want to draw inference (reason from particular sample to general population using P-values, confidence intervals, etc.) then not so much.
I'm sure there are topics in SE on Kalman filters.
I'm not sure that there will be a clear starting point or a proper reference exactly. But to understand the odds ratio better, it might help to read my answer here: [Interpretation of simple predictions to odds ratios in logistic regression](http://stats.stackexchange.com/a/34638/7290).
@Flounderer, tried to address that in corrected answer, but probably not.  But McCulloch-Pitts was the first back in the 40s, and so all neurons inherit from those early works.  I doubt you will get extra gravitas by giving it a grand name though.  And if you get mixed up you look a bit of an idiot (like my first answer really...)
Just the sum across all items of a scale. (Or use the mean instead.)
should say "when the negative log-likelihood is *minimized*" (or *optimized*).
The definition of the random variable is still unclear. Are you saying that the random variable is the product of the sum where one aij is missing from the sum and the choice of the missing number is randomly selected as the one left out?
Alecos, that's a reasonable answer, but it does not help my particular problem -- as I said in my point 1, I know how to devise a greedy search (and that's what I am doing now), but that's a mechanical solution, not a statistical one. Besides, the residuals are not really observed, as these are limited dependent variable models, as I explained in the update. I also want to keep the regressors separate, as that's what the existing evidence points for the fit of the separate equations.
But you say you have 90 observations and 15 unique stimuli; would you clarify?
(1) first you say you're forgetting the limitations of number of observations then you mention them..? EDV usually plays a part in model building (2) I think the rest is less a statistical issue than knowledge domain issue.
@DikranMarsupial, Ha, apologies to Tukey. I copy-pasted the reference and I didn't notice the misspelled name. Yes it is definitely Tukey.
great (better) answer.
Huh? If you can exhaustively generate all permutations, then you can answer *any* question of that sort with perfect accuracy. Generating, and re-generating, far more permutations than you need is just a complete waste of computing time and will give you less accurate answers.
This question has a different objective, @Michael.  It sounds like you have not really read my answer, because you seem to have adopted a different model of what is going on.
Looks like a good first book. And, there is a Kindle version.
@whuber - changed my second paragraph, it was an ill chosen word.
yes $\\beta_0$ is the intercept
As @Gavin said, one of the potential pitfalls is that decision trees need a large sample size and are quite unstable (which is one of the reason bagging and random forests were proposed as viable alternatives). Another problem is that it is not clear whether we seek for second- or higher-order interaction effects. In the former case, CARTs are not a solution. In any case, I will find very doubtful any interpretation of an interaction between 6 variables in any kind of study (observational or controlled).
@cardinal good find. thanks!
I don't see why this is off topic.  Looks like a reasonable question to me.
Also a basic explanation is here: http://effectsizefaq.com/2010/05/30/what-is-the-difference-between-statistical-and-substantive-significance/
Did you try to run the various examples in the help of the package?
This is now for time series experts. I am not one, but I see no reason to revise my comments.
I don't think it's a definition or characterisation of descriptive statistics that they aim for minimum loss of information. It's entirely possible to have descriptive statistics that leave out really important detail and that's often a problem.
This is wrong.  What population variance does it represent?
I am interested in lossless compression.
@Douglas: You are correct. Thanks for that!
Thanks, the link is a good presentation on the subject, just what I was looking for.
+1, however, I suspect the ANOVA is the p-value from the initial full model, meaning that it is a valid p-value.
I wonder if the machine learning that goes into Google Search and other Google products to show us adverts while we read our mail would be counted by the OP?
What is the purpose of your question? Why do you need such a list? Doesn't google search on statistical packages on linux answer your question?
Can you expand on what is being measured? When you say you have three 'experiments' for each condition, are you referring to three replications of the same conditions?
I think that this analysis is correct. You could lose some efficiency and/or introduce bias in small samples, where you are doing extra work, but asymptotically it should not matter much if the information aligns the way you describe.
(yes, the gene expression values are more or less normally distributed)
According to my pretty blur memory, Mplus may have AIC and BIC for some models based on survey data. You can take a look at its website: 

http://www.statmodel.com/

There is a discussion forum on the website. You can post this question, and Dr. Muthen (the developer) will answer you. Good luck!
By 'residue' in your title, do you mean 'residual'? If not, could you clarify what it means?
Thanks for your answer. I've edited the question to make it less general.
In practice sometimes there is no alternative but to extrapolate outside the data, but it requires very strong assumptions (that a model will continue to be equally adequate over the wider range), and even so is subject to a rapid expansion of confidence intervals. The model may be badly wrong and there's no data to check the assumption. However, in some cases you can be *sure* that extrapolation must eventually be wrong -- and your example is a good one. If you fit a linear model where price is decreasing with mileage, eventually there's a predicted mileage where the price is negative!
so you did a finite mixture model, and that found a mixture of several Gaussian densities, so each observation should now belong to one of these distributions identified in the finite mixture model. What I have done before is make a new variable in the data which is the group each data point is assigned to in the mixture analysis and treat it as a known group, then use that as the input into a discriminant function. A discriminant function works off a distance matrix, you classify each observation to a group if its distance to a particular group mean vector is the minimum of all mean vectors
I am only using 18 in total with this set (it is a learning set for me before I tackle bigger sets)
Why are you fitting a Poisson model using weighted least squares rather than maximum likelihood?
Ah, but you know much more than that, because after using logs in regression, you know that the results are interpreted differently and you know to take care in back-transforming fitted values and confidence intervals.  I'm suggesting that you might *not* be confused and that you probably already know many of the answers to these four questions, even though you weren't initially aware of it :-).
Doesn't the 2.21 here mean for this dataset and the method used (so that combination) 2.21 is the cut-off below which the eigenvalue is too small? I am not sure what you mean by "sole value." Do you mean as a general rule, like the eigenvalue 1 rule? The cutoff is different for each parallel analysis typically.
I think the Multinomial distribution might help you...see here: http://en.wikipedia.org/wiki/Multinomial_distribution
@Scortchi yes, I am quite sure, the demographers use those terminology to distinguish different aspects. I downloaded a pdf  from internet where they have just mentioned that there is difference but not explained what is the difference. Also The book `The Methods And Materials of Demography`  by `Siegel & Swanson` gave the difference of `projection and forecast` but it is not clear to me.I wish of an example(could be mathematical example of artificial data).
Thanks for your response John.  I'm not sure how I would go about using mixed-effects modelling to test from equivalence - do you have any examples?  Thanks!  Sam.
That's correct about the ML bent.  Great suggestions. Thank you.
Are you talking exclusively about "central tendency" measures? Like mode... or trimmed means.
There's now a diagram for that, with comparison to shifting. @AndreSilva Barring further questions or corrections I think it's done.
@drfloob search for "poissonization", a useful device in urns-balls models. Basically, if you throw N balls into M urns, you have a multinomial distribution, with $\\sum n_i = N$ and $E(n_i)=N/M$. It can be shown that if you consider instead the (different) distribution of $M$ iid Poisson with $\\lambda =N/M$, this distribution conditioned on the event $\\sum n_i = N$ is exactly equivalent to the previous multinomial. And, asymtotically, that event gets more probable (informally speaking), and hence both distributions are asymtotically equivalent. This have several generalizations.
No idea if this is what is causing your problem or just a copy/paste error, but the syntax you are using in the assignment of `jags.inits` is wrong.  It should be `list(foo = bar)`, not `list(foo <- bar)`.
Do you mean coefficient *b* or coefficient *a*?
Perhaps 'tails' here are identified with respect to the median, not w.r.t. the mode. If ${\\mathbb P}(X>a)>0.5$, then this is interpreted as: $a$ lies in the left-tail of the distribution of $X$.
I disagree. Vanilla LDA without incorporating priors (as they do in the paper I was commenting on) or using labeled data after the fact would absolutely have a problem identifying any kind of sentiment reliably. Why? Because there's no reason to assume the topics found by LDA have any relation to sentiment. In regards to your example, LDA represents documents as a bag-of-words and is therefore subject to the same limits on ability to use context as any other method (supervised or unsupervised) using this representation.
@cardinal Some players play only one team, others play a couple, and some use a randomizer built into the game. I'm not sure of the ratios.
+1 I totally agree with Wayne.  To be honest I think the `auto.arima` has done the right thing really, it has just said your best guess of the next number is the previous number.
You can calculate them... you just can't compare them to critical values unless the two series are bi-variate normal
@Michael: the uniform distribution of the p-values under the null hypothesis was something I was unfamiliar with, so it seemed like a great revelation. Do you know of any books that point this out. That part of my answer was not relevant to your contribution, but it was relevant to Peter's contribution.
If I'm not mistaken, that's the Cox & Snell pseudo R-squared. For explanation & comparision of various pseudo R-squareds, see http://www.ats.ucla.edu/stat/mult_pkg/faq/general/psuedo_rsquareds.htm .
Anybody care to give suggestions for improvement rather than silently downvoting? This seems like an interesting (but vaguely stated) problem. Some more information would be helpful Andrew - what exact question are you asking? "The good price to buy stuff" could depend on a ton of things - most importantly, the market value of the item. Are you asking something more specific?
Firth's penalized likelihood methods could be used to address your separation problems, but as a non-SAS user, I don't know whether it is available to GLIMMIX (see http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect063.htm and references). For technical details, see http://www.meduniwien.ac.at/user/georg.heinze/techreps/tr2_2004.pdf
Probably because the OP only gives a link to where he's seen the terms "nominal" & "actual" (rather than summarizing or quoting from it in the question as you did), & then devotes the rest of his question to his misinterpretation of their use in that context.
No, any compound Poisson process has an exponential waiting time. This means you add $\\text{Pois}(\\lambda t)$ IID random variables with some distribution.
@cardinal I think you meant to say "Yes, $\\vec{Y}$ is a multivariate _normal_ random variable".  That $\\vec{Y}$ is a multivariate random vector is a tautology.   But this is an issue that comes up repeatedly on math.SE as well as stats.Se where many assume that marginally normal automatically means jointly normal as well and many assume (as eakbas has done) that joint normality requires independence.  The overlap between the two groups of people may be large too.
The book contains a typo, obviously, if one checks how the log-normal density is _defined_ , http://en.wikipedia.org/wiki/Log-normal_distribution
My gut feeling reading this question was that convergence failure should only go in methods IF it helps clarify/justify the use of other methods or techniques.  Your answer confirms that this seems to be what others do as well.
Correct me if I am wrong but `from=0` looks as if it just suppresses plotting for values below 0; it doesn't correct the calculation for the fact that some of the distribution has been smeared below 0.
This is simply a re-posting of your previous question. Please edit your previous question to make it clear & answerable. Do not simply re-post a question that cannot be answered.
The coefficients will be the same, R's default coding for factors is exactly as you described (this is called "dummy" coding). If doing this manually, you will need to aware of the the "dummy variable trap" - you cannot include all $N$ created variables, but only can include $N-1$ (alternately, exclude the intercept); otherwise your model is overidentified. There are also other methods of encoding factor variables as well.
Really appreciate your solution and the others as well.  You mention that:  **However if it turns out that the means of time series are different (test it, since in this case @Rob Hyndman's method should fail!)**  Can you explain more about why this is?  Thanks.
@Glen_b: I see how the confusion came to be. I edited my post to make it more clear.
Can you please give any reference for this, for above mentioned case I could not get appropriate help ...
@ttnphns - Very helpful.  Thank you.
In statistics, the term kernel is typically used in the context of density estimation. In machine learning, as is the case for kernel logistic regression, kernels are functions that define an inner product in *some* feature space. As far as I know, there is no direct relationship between the two uses of the word kernel, which can be quite confusing (especially when talking about Gaussian kernels).
I don't understand the statement, "the diagnosis codes may be different for some of the billed amounts". Can you clarify what that means?
Old question, but this thread helped me just now, so here goes:  The logit observes 0 or 1, but it predicts a probability.  When you get a standard error of a fitted value, it is on the scale of the linear predictor.  You get a confidence interval on the probability by talking logit(fit+/-1.96*se.fit)
You're right, but after re-reading the question several times I still do not see that it suggests removing the outliers altogether.  (I have given it a new title to help make its intent clear.)
Formula B only gives the residuals if the model has no reference to treatment at all.  Obviously this would be no good to fit as the only model we test (or, as you say, why bother with the experiment).  As I said in the answer, sometimes this is our null hypothesis (treatment has no effect) so it should be taken seriously, but it isn't the model we think that best fits the data unless the treatment really has no impact.
I disagree with this answer. Using a subjective prior is also a parametrization invariant procedure !
I really like how you explained the difference between them by explaining how you would sample from each.
@sino Not in general (that I know of).  There may be some generic independence tests based on [copulas](http://en.wikipedia.org/wiki/Copula_%28statistics%29), but the example of the multinormal distribution shows that you can expect to obtain more powerful tests by exploiting assumptions about the marginal distributions.
I don't know them well enough to explain each of them. It's just clear that the don't have a comparable scale / unit. Just like Volts and feet are not comparable.
Have you contemplated some particular examples?  For instance, is it the case that $\\log(1)+\\log(1)=\\log(2)$?
Not really my area but this 'might' be relevant?
Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006; 1:515–533. http://dx.doi.org/10.1214/06-BA117A
Are you, perhaps, asking about why the EM algorithm works for HMM, i.e., via an application of Jensen's inequality and what role concavity plays in the proof?
And what makes you think there is hardly any agreement? The most frequent emotion for each clip in different groups?
I think G is for Gaussian.
+1 Thanks for posting an official reply.
1) What do you want to discretize and why? In this discussion you haven't explicitly mentioned any continuous variables so far. 2) I'm not sure but I believe `spline ordinal` is a more restricted model than just `ordinal`. I don't know about the exact details. P.s. if you find answers/comments helpful you can upvote them.
the problem is that 'conditional on $x$' forces $y-\\hat{\\beta}x$ to be just a shift of $y$ and so $R^2_{\\mbox{ach}}$ is just identically zero as defined. 
As noted, I will later have to consider $x$ as being normally distributed, in which case, I believe, the achieved $R^2$ as defined is no longer forced to be zero. bleah.
You can define outliers without a model, but I have found such definitions to be unhelpful.  BTW, by model, I don't necessarily mean a statistical model that has been explicitly fit to the data.  Any definition of an outlier requires you to make some assumption about what sort of values you expect to see, and what sort of values you don't expect to see.  I think it is better if these assumptions (i.e. the model) are made explicit.  There is also the point that in EDA, you are exploring the data, your definition of an outlier may be very different for EDA than for fitting a final model.
(ctd)... you'd need some estimate of standard error (which I don't see in the plot there) to judge significance.
I would suggest to provide additional details, especially regarding the "quite strange results" you got.
@user5334 Yes, exactly ("before marriage index" would have been more English ? I have learnt a lesson :)
I'm guessing that your question is about the nature & calculation of the Gini index, & not about how to implement that in SQL (correct me if I'm wrong). If the latter, we should migrate this to SO. Continuing w/ my assumption, I have copied your code from the SE data site, but it might help if you can also rewrite it in pseudo-code for those who may not read SQL well.
Reasoning from first principles, a symmetric distribution for such a variable $X$ will be completely determined by $\\Pr(X=-1)=\\Pr(X=1)=p,$ with $0\\lt 2p\\lt 1.$ *All* possible distributions are determined by two parameters $p\\gt 0,q\\gt 0$ with $p+q\\lt 1$, where $\\Pr(X=-1)=p$ and $\\Pr(X=1)=q$. These simple observations are so evident that I wonder what you are really trying to get at in this question: are you looking for information about choosing a family from among these distributions in order to model some phenomenon? If so, then please share information about that phenomenon.
@Giancarlo Because it is asked to use MCMC and I don't know how to use it over this region. Thanks for your help, btw
Showing standard errors or, similarly, Student confidence intervals could help the reader to give an impression of the precision of your estimates.