tttjjj/elt_bench huggingface dataset:
This is not true if you are not looking at all lags. The best example is a sin(x), cos(x). Think of two series. One of the cumulative sum of sin(x), other cos(x). sin(x), cos(x) will give zero correlation at 0 lag. But the cumulative sum series will be cointegrated. Moreover as far I as know you can build a nonlinear conintegration. I can't construct an example but I think one can find a relation that will have zero correlation on all lags for differenced series.
The number of parameters for each model is the same, so comparing AICs reduces to comparing log-likelihoods; a reasonable enough thing to do. But don't expect much difference.
Welcome to the site, @user37364. Thanks for these links. Would you mind elaborating on them a little, in case of linkrot & so future readers can judge before clicking if they want to pursue them?
Hi Peter, thank you. I have bene using scatterplots and qq plots but I am trying to find resources to learn how to interpret them. Summaries are good because it is easier for someone to infer their meaning. Do you know any book or article that discuss more about these different methods for *non-parametric association*?
I'm not accepting the answer as I'm not very happy with it, but since the question didn't get much (any...) attention, I figured it's better than nothing.
In what way is the question related to Spearman correlation?
I've added a sample output in the question :).
I think you mean dependent variable.
You have said you will use the same patients for the second round, but will you also use the same observers? Will each observer read all 50 X-rays for both rounds? If not, how will the X-rays be assigned to observers? Approximately, how many observers are there?
I just modified my formula with `lm( I(log10(A) - log10(B)) ~ I(log10(A) - log10(C)))` and worked out fine. Thanks very much!
(@charlie) thank you for ur advice. (@ttnphns, @Karl): the slight discomfort that i do have with this is that: Not having enough evidence to reject H0 is not the "same" as having evidence to support H0. I guess this relate a bit to type I and type II error.
@PeterFlom The sampling situation described sounds like [multivariate hypergeometric](http://en.wikipedia.org/wiki/Hypergeometric_distribution#Multivariate_hypergeometric_distribution) to me, not multinomial, since the pieces are sampled *without replacement*, in exactly the way that the hypergeometric relates to the binomial when sampling without replacement. Picking the wrong one can make a substantial difference in this kind of problem, since the closer you get to getting all the pieces in one puzzle, the harder the last few pieces are to get.
@Christopher You can also do this with `ggplot2` (esp. if you don't need to draw country boundaries), http://had.co.nz/ggplot2/coord_map.html. Otherwise, `maps`, `gmaps` are better. There's also `GeoXp` and an R interface to GRASS. BTW, Mondrian has a plugin for geographical data :)
@semibruin : My question is not about selecting the number of knots but the knots themselves. I would like an estimation procedure which would give me the points in the data where the slope of the line is changing.
hey look at the edit kartik
Some smart-aleck is going to answer "Yes" (which, incidentally, I believe is the right answer).  Is there more that you are really intending to ask?
I thought just using OLS or something, but the actual values of $\\beta$ _are_ 0 and 1 (From the data set I have). Also when I do that, many times I get negative values for $\\beta$, which is not very good for what I'm doing.  I'll look into your suggestion of t-stat -> P-value though, I don't know anything about t-stats and p-values.  Thank you!
@jbowman: Thanks! It makes me think of another question: Suppose a MC reaches its limiting distribution at time t. Is it correct that all the points (i.e. draws) in the same run (i.e. same sample path) after t are not necessarily independent with each other, but those points/draws that are sufficiently far apart in time are?
You can't outpredict the stock market. If the price for Google is X, then the odds it will increase or decrease are equal given all the information that is available (w/o insider trading). There are big investment houses with large staffs of Ph.D.s working 80+ hours per week looking for some scrap of information about whether Google (eg) will go up or down. If someone figures out that the odds of increase are >1, they will buy Google until the price reaches the equilibrium value. The odds will always be 1 for both increase & decrease for all stocks.
On one side, representing the categories is more properly done using the centroid of them, that is, the averaged vector across all the vectors for the document. In fact, the Rocchio algorithm does so for relevance feedback in Information Retrieval. On the the other side, you may think of using multi-document summarization for representing each category, then using the cosine similarity.
Some thoughts: (i) Gaussians won't do it.  You're interested in the right tail of the distributions (winds below a few km/h won't generate any power).  (ii) You don't need 100 quantiles; you can interpolate.  Probably 5-16 quantiles will do well.  (iii) You only need a few bits per quantile (8 will do) because economically useful wind speeds lie in a small range.  (iv) 1000 distributions / 15 min * 15 numbers/distribution * 5 E5 min/yr = 5 E8 values = 0.5 GB per year.  That's worth about $0.50 today: you can afford it!
You might find this [King & Zeng paper](http://gking.harvard.edu/files/abs/0s-abs.shtml) on the rare events logit helpful. They also implemented this approach in Stata, Gauss and R (through Zelig).
Please add more details. Calculating correlations is only the first step, so the question depends on what are you going to do with these correlations.
$\\lambda = \\mu = 0$ will be equivalent to a log transformation but in the general case, it is going to be different. You can try to run the example with any vector $y$ and $x$ that have the same size.
First of all, you might want to define what it is that your variables mean. What are Y, u and y?
Please explain your abbreviation (RVM) on first use to avoid ambiguity. Most such acronyms have multiple interpretations (statistics is a big topic, used in a variety of areas).
Have you consulted "Econometric Analysis of Cross Section and Panel Data" by J. Wooldridge? Several period DID models are surely mentioned there. The jumping individuals may be treated with appropriate dummy variables.
@whuber: I accept the challenge! :-) 
Although note, since all researchers start their lookups in Wikipedia, it is a matter of time until credible peer-reviewed journals align with the Wiki definition. ("if you can't beat them...")
There has been some recent research on constrained clustering.  Google http://www.google.com/search?q=constrained+k-means .
Please, don't forget what I've said: the unclustered probes will continue to "contribute", but as noise points only. I.e. they still "spoil" cluster separatedness, but they themselves aren't clusters anymore.
Thanks for your answer. What I was thinking is that by simply using "regular" dummy variables, the number size of new variable (zip code, for example) would be way bigger than this simple 10x1 representation.
Was this accidentally posted in the wrong place?
That you mentioned the [Gifi](http://bit.ly/AtTT8O) team (aka Leiden univ. *nom de plume*) and Greenacre's work is really good! I'm just re-reading the [book](http://bit.ly/ykhUPf) where the authors provide extensive discussion of your last paragraph. My big +1.
-1 because you don't state what you have tried to answer the question, nor given any indication of where you're having trouble.
@gung VC probably refers to [VC dimension](http://en.wikipedia.org/wiki/VC_dimension).
Could you please explain what meaning the $-1$ values have "from the performance perspective"?  Do you want somehow to incorporate the rate at which flawed attempts occur into your assessment of changes in throwing ability?  (If so, this is not pure censoring and needs a more careful treatment.)
@murekatt, if you do not have additional data on demand, but you need the model for price, you deal with this by taking extra care. This means less attention to statistical significance of coefficients, but more attention to forecasting performance. Essentialy this means treating regression as black-box and use the model forecasting performance as measure of model validity. This means using cross-validation, data division to train and test samples, etc.
It is true that you _can_ use the least squares estimates of $\\beta$ to predict, I think it is worthwhile to mention that, for example, a ridge regression estimator (or another regularized regression estimate) of $\\beta$ may provide better prediction than the least squares estimator and would also not satisfy $E(y|x) = \\beta_{0} + \\beta_{1}x$.
Ok, so.. its not *my* approach, I pointed out two places I have seen this.  The approach is the crux of the issue.  What is the difference between the multivariate version and the reparameterized univariate version?
I would call it a fixed effect because it make no sense to estimate the distribution of the batches, mainly because there only two of them. The linear model in my previous comment compensates for the batch effect. You'd probably get more comments if you documented the structure better, if there is any (as opposed to my guess above).
This seems the wrong way to go. As the same correlation could reflect different linear relations, and vice versa, you would be moving yet further from the data, and correlations really don't seem a natural measure to average. What is the scientific question underlying all this? It can probably be rephrased in terms of a generating model.
Hint: The marginal density of $f_Y(y)$ is the _integral_ of $f_{X,Y}(x,y)$ which, for a fixed value of $y$, $0 < y < 1$, is nonzero only for those $x$ satisfying $y < x < 1$. That is, $$f_Y(y) = \\int_{-\\infty}^\\infty f_{X,Y}(x,y)dx = \\int_y^1 15xy^2 dx$$ and that is where the $(1-y^2)$ part comes from.
@Glen_b Why the p-values don't apply? Three one-tailed proportions can be used, however the significance of all the three conditions holding might be significant while non of the single hypotheses' aren't.
Glad I could help. You don't have to wonder; you can always look at the source code of `forecast`.
I edit to include the means and std of the samples, but doesn't this "overlapping coefficient" give more information? If I understand it right, the 5% overlapping area is similar to rejecting the null hypotesis with a 99.75% condifence...or not?
@JoelW. Please have a look at my updated question. thanks.
Sorry, @ArunJose, I probably wrote the question in a misleading way. I'll correct my question. I intended to say that, I have my full dataset as a reference. Then, I create $n$ datasets by cutting away some samples from each. Each of these subsets is created independently with respect to the others. Then I compare the classification of the common samples between the full dataset and the each one of the subsets.
How does that make classification the right thing to consider?  Just because your instructor mistakenly believes it so?  Consider this example: Suppose an outcome occurs in 950 out of 1000 individuals.  Classifying every individual as having the outcome, you will be right 0.95 of the time using no data on the subjects.
I can reproduce the problem. The reason seems really to be numerical instability, as changing C0=matrix(0) to C0=matrix(1E-12) allowed me to get results very close to yours.
Great! Please note that you have not signed in from the same account as when asking the question. Please do so and accept the answer. Good luck!
But it is the case that if one of the principal components have coefs all of the same sign, then this cannot be true for any of the other principal components. 'Maybe thats what you asked to prove? (hint: If two principal components both have all coefs the same sign, how can their interior producyt be zero?
That doesn't follow, and will be terribly inefficient.  You can use a robust continuous model including a semiparametric model such as the proportional odds model.
Excellent idea, thanks. I would upvote but lack the rep - hopefully someone else finds this useful and does so.
Sorry, I tried to keep it concise without giving too much detail.. The p-values and partial R-squared values come from the second model (with five variables). But thank you for the suggestions; I'll look into them for sure (ridge regression and so on). But why would partial R-squared have more problems than R-squared? Because of the other potential adjustments that are made?
Google search found a [JSTOR paper](http://www.jstor.org/pss/2286664) for linear models in finite populations. Substituting your figures should decide if the difference between the finite and infinite population setting is negligible or not.
For $L_2$, I'd say "spherically symmetric."
Good point. I would also like to point out that many recent improvements continue to be made in the heuristics underlying model identification so that any 13 year old competition results (from 1 and only 1 origin) may no longer be relevant.
I think it is worth examining the actual pattern of association between income and fact/amount of purchasing of each product. Look at the scatterplots and nonlinear approximations on them. You might find that some products do not depend on income, some depend smoothly and some abruptly.
I downvoted this because it encourages uninformative one-line answers, leading merely to a collection of links with little additional value.  If respondents were to explain, even briefly, *why* their proposed conferences were "significant," this thread would have tremendously greater value.
maybe I should try SVM, by dividing this dataset into in-sample and out-sample? Or may I should try leave-K-out, etc.?
It's been filed as a side project when I finish my dissertation, but yeah, hopefully I'll be able to do something with it.
@baixiwei because the ANCOVA tries to remove the influence of `study` from the complete analysis. This however, doesn't make any sense for the `pretest` scores as detailed by Miller & Chapman. However, their argument does not apply to the `posttest` scores. hence, we perform a kind of manual ANCOVA only on for the posttest scores, but leave the `pretest` scores intact. So this is a kind of ANOVA - ANCOVA hybrid. Pretty rad if you ask me...
See John Fox's response here: http://tolstoy.newcastle.edu.au/R/help/05/11/16368.html
I think you mean the sample proportion $\\hat{p}=k/n$.
Plotting the average of response versus age is where I'd start. Next level might be showing the fractions of transitions 00, 01, 10, 11 at each age.
The vectors should be sparse. I suggest you choose a fixed palette of 10 well-discernible colors, and assign them to the top 10 words for the image, and hide all the rare labels. If the resulting vectors are not sparse, the bag-of-words approach already failed.
(+1) In light of your improved explanation, I have to agree.  But before that explanation was available, there wasn't any good basis to decide which to favor.  I'll take a well-reasoned theoretical argument any time over simulation results :-).
Random forest, SVR... For feature selection you can try some all-relevant method (should be more stable) like ACE or Boruta.
@Michael That's an important point. I am encouraged, though, by a comment by the OP mentioning an *ad hoc* piecewise linear fitting procedure. Splining works in the same spirit but with greater flexibility and rigor.  Quadratic terms *might* work but that seems like a lot to hope for.
Each subject (curve) is profiled by fitting a lowess smooth to it and evaluating the smooth over a fixed grid of points, in case different curves had different sampling times.  Two points would characterize a linear relationship, three a quadratic.  $p>3$ is usually recommended and $p=10$ is not a bad choice.  The interpolated ordinate at the grid of points is fed into the clustering algorithm along with maximum time gap, number of missing values, and other things.  $p$ can be larger than needed without doing much harm; it's ok for some characterizations to be redundant.
Too many questions to answer in comments. I've written a blog post at http://robjhyndman.com/researchtips/cyclicts/ which might help.
I'm not sure why the ratios are important.  Are you trying to just determine if a change 'significantly' lowered system or real time?
$\\lambda$ can only have positive values for a Poisson distribution, it would not be normalizable otherwise right? It would also be physically irrelevant, as a Poisson process with $\\lambda<0$ doesn't really make sense. Can you also explain your last sentence as well in more detail, possibly in an answer form?
Is there temporal correlation within each signal? or across the two signals? If so would a simple transformation generate i.i.d. samples (e.g. a difference)?
Perhaps it would help if you told us what package "ridge" is in.
@Glen_b PowerPoint :-).  The image of a box is from http://mymiddlec.files.wordpress.com/2013/09/empty-box.jpg.  The tickets are PowerPoint graphics.  (There's nothing impertinent about such questions!)  I grouped the whole bunch, pasted it into Paint, and used that to save it as a .png file.
This answer is riddled with errors. After spending ten minutes trying to fix them all, I gave up and abandoned my edit session. I fear this is beyond saving.
@AndyW Say, customers a, b, and c are rating objects 1-100 and I want to combine each of their ratings into a single set of ratings for the 100 objects. A direct mean of the ratings of each object may not work well if each customer used the rating scale differently.
Could [Procrustes analysis](http://en.wikipedia.org/wiki/Procrustes_analysis) help your point 1 (comparing how close the model shape resembles the input shape)?
That was my first guess, but is not the case… The crosstabs look good, I have missing cases but I have 17% of the cases in the level that is dropped. I also tried to change the reference category, the result is the same. I tried to change the measure (ordinal, nominal, scale), the same result...
@Glen_b, the variables are all coming as "factor" when I cheek their class type. I thought R would interpret text as a string and numbers as integers or ideally time int his case . My assumption was wrong though.
Luna, it may be time to look back to earlier responses that were given to you, and start upvoting/accepting answers, no?
For background see http://stats.stackexchange.com/questions/103984/estimating-uniform-distribution-endpoints-using-data-with-errors/104013?noredirect=1#comment201005_104013
I believe to do this properly you will need to use time series analysis. Perhaps, apply ARIMA and look for a trend. How much data do you have?
@Dominik, I edited my response to try to answer the question you raised.
Another one is a [linear prediction model](http://en.wikipedia.org/wiki/Linear_prediction) of the data, being simplest (more elaborate models of course exist)
`downplay, but not eliminate, gender's role in the analysis` This request is contradictory and strange in itself unless you specify the _degree_ to what downplay gender's role.
A toy example would be this: Bayesian inference, you want to make inference toward the form of a distribution. Prior allows two possibilites : H1: Distribution is standard normal. H2: Distribution normal, mean = sd = 1. A sample of the values of the distributions allows you to update your prior. When you are given just the signs of your values you can also update your prior, but the update will be less informative since you threw away relevant information.
Very significant considering a lot of the population back then were rather isolated from each other, so there was far less opportunity to avoid intermarriages.
Peter, when you can just link to another answer, the question almost certainly is a duplicate and ought to be closed as such.
Perhaps of some interest: [Link](http://cran.r-project.org/web/packages/urca/index.html)
Do you want the distribution to be unimodal or not? The title says so, but the text barely mentions this point.
The main effects is new and has not been studied before. Many different sample sizes have been used before on the control variables -not sure how it affects the results. Parameter estimates  always vary, but I am not sure how big a variation actually mean something...
You ask specifically about multi-class LDA. What makes you think that multi-class LDA and two-class LDA behave differently in this respect (under violation of normality and/or common covariance assumptions)?
I think what you did makes sense, but I'll let it to more experienced people to comment. Yet, I'll have to stress again the same issue, I am sorry, as I repeat myself and this is certainly heavy, but I think the way you bootstrap is not clear. You write that each grid has 500 regions; and then that you draw 500 regions with replacement from this. So this means you will for sure repeat some values but I am not sure this provides a correct assessment of the grid intrinsic variability, and this is central to the deduction that follows.
I have placed a link to the article I think you are referring to as item (1) under the references, but since Efron & Morris wrote a whole host of articles on related topics during that time period, it's somewhat unclear which one you were actually referring to. I've also tried to adjust some of the formatting and spelling. Please check that I haven't inadvertently introduced any errors and feel free to edit further or rollback any of mine.
I tried it ever which way, maybe I need to remove a layer of indirection and post that question instead. .)
Don't perform an analysis that we _know_ is suboptimal.  The fact that it has been used countless times in the literature does not predict the probability that it is a good idea.  As the person who developed the $c$-index I've had a fair amount of experience in this area.  Think of $\\Delta c$ as comparing two Wilcoxon statistics for A vs. B and A vs. C to compare B vs. C.  No one would do that.  And the inventor of IDI and NRI recently stated he would never use them for statistical tests but would instead use the likelihood ratio $\\chi^2$ statistic.
@GaBorgulya: well, I thought of doing some kind of histogram of the frequency distribution in the 3 blocks, but I'm not sure how to integrate the power information in that...
It depends on whether $p$ was estimated from these data or was determined in some other way. Perhaps you could clarify that point? (Fortunately, in this case you will get the same results no matter what: the count for $A$ is so obviously out of line with a frequency of $0.01$ (predicting a count of only $5$ instead of $35$) that it is extremely implausible that these data are consistent with $p$. You can use a Poisson or Binomial or even Normal approximation to get a sense of how extreme $35$ is; the calculations are easily carried out in your head.)
Homework tag too?
You don't have to "tweak R". You can use the argument `ci.type="ma"` to get Bartlett standard errors.
@rolando2 I don't know, unfortunately. That book chapter cites "Olsen & Forsberg, 1993" for that claim, which I can guess is [this chapter from Visual Processes in Reading and Reading Disabilities](http://books.google.com/books?id=o4TjKN1fZ3MC&lpg=PA377&ots=_VHl9Csgyl&lr&pg=PA377#v=onepage&q&f=false). [This paper](http://psycnet.apa.org/journals/xlm/21/6/1430/) also backs up the claim.
Don't forget accept an answer in the future, not necessarily mine.
As a starter, don't you think it's a bit unclever to estimate the means with $\\bar{x}$ and $\\bar{y}$ when in fact we know that they are 0 and 0?
Hope so... ;)  .
This answer is good, but based on the comment stream it's not clear that it actually responds to the doubts of the OP. (Some further clarification from the OP would help.)
This question covers similar material & will be of interest to viewers of this page: [Is there a minimum sample size required for the t-test to be valid?](http://stats.stackexchange.com/questions/37993/).
`it has no such interpretation` Yes MSE has no, but RMSE has and, moreover, is comarable with MAE. It is this comparison which is often of prime interest.