Re: GARCH
I think you can try XploRe please visit http://www.xplore-stat.de thank, Hizir "[EMAIL PROTECTED]" schrieb: > Could anyone tell me how to > perform GARCH analysis on > time series data? What software > do I need to perform this type of > analysis? Any help would be > greatly appreciated. > > Thanks, > Tom Curtin Sent via Deja.com http://www.deja.com/ Before you buy.
could someone help me with this intro to stat. problem
5 of 10 volunteers are randomly selected to receive self-defense training. The other 5 receive no training. At the end of the training period, all subjects complete a self-confidence questionnaire. a.) Is there a difference in self-confidence between the 2 groups (p<.01)? b.) What are the effects of self-defense traing on self-confidence (I'm assuming a two-tailed test?). Explain analysis Please help, I can't figure it out...my mind has gone blank
Re: Disadvantage of Non-parametric vs. Parametric Test
- I have a comment on an offhand remark of Glen's, at the start of his interesting posting - On Tue, 07 Dec 1999 15:58:11 +1100, Glen Barnett <[EMAIL PROTECTED]> wrote: > Alex Yu wrote: > > > > Disadvantages of non-parametric tests: > > > > Losing precision: Edgington (1995) asserted that when more precise > > measurements are available, it is unwise to degrade the precision by > > transforming the measurements into ranked data. > > So this is an argument against rank-based nonparametric tests > rather than nonparametric tests in general. In fact, I think > you'll find Edgington highly supportive of randomization procedures, > which are nonparametric. > - In my vocabulary, these days, "nonparametric" starts out with data being ranked, or otherwise being placed into categories -- it is the infinite parameters involved in that sort of non-reversible re-scoring which earns the label, nonparametric. (I am still trying to get my definition to be complete and concise.) I know that when *nonparametric* and *distribution-free* were the two alternatives to ANOVAs, either of the two labels was slapped onto people's pet procedures, fairly indiscriminately; and a lack of discrimination seems to have widened to encompass *robust*, later on. Okay, I see that exact evaluation by randomization of a fixed sample does not use a t or F distribution for its p-levels. Okay, I see that it is not ANOVA. But, I'm sorry, I don't regard a test as nonparametric which *does* preserve and use the original metric and means. Comparison of means is parametric, and that contrasts to nonparametric. Similarly, bootstrapping is a method of "robust variance estimation" but it does not change the metric like a power transformation does, or abandon the metric like a rank-order transformation does. If it were proper terminology to say randomization is nonparametric, you would probably want to say bootstrapping is nonparametric, too. (I think some people have done so; but it is not widespread.) -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html
Coefficient of Determination Question
I am looking for the coefficient name for (1-r^2). I know r^2 is the Coefficient of Determination, but I do not know the name of the (1-r^2) coefficient. Any assistance would be greatly appreciated. Thanks in advance GM
Re: could someone help me with this intro to stat. problem
On 8 Dec 1999, Luv 2 muah 143 wrote: > 5 of 10 volunteers are randomly selected to receive self-defense training. The > other 5 receive no training. At the end of the training period, all subjects > complete a self-confidence questionnaire. > > a.) Is there a difference in self-confidence between the 2 groups (p<.01)? > > > b.) What are the effects of self-defense traing on self-confidence (I'm > assuming a two-tailed test?). Explain analysis > > Please help, I can't figure it out...my mind has gone blank Without a pre-test measure of self-confidence, taken prior to the training, even if there is a significant difference post-training, it's not possible to tell whether the difference is the result of the training or was there to begin with. If there is a pre-post measurement of self-confidence, then you need a mixed model Anova, with Training vs. No Training as the between groups factor and Pre-Post as the within groups factor. Mike
Re: Coefficient of Determination Question
Sample coefficient of alienation "Gaurang Mehta" <[EMAIL PROTECTED]> wrote in message 82m788$[EMAIL PROTECTED]">news:82m788$[EMAIL PROTECTED]... > I am looking for the coefficient name for (1-r^2). I know r^2 is the > Coefficient of Determination, but I do not know the name of the (1-r^2) > coefficient. -- Peter Gieser, PhD Biostatistics Core, Cancer Control H. Lee Moffitt Cancer Center & Research Institute at the University of South Florida 12902 Magnolia Dr., MRC-CANCONT 208 Tampa, FL 33612
Re: Coefficient of Determination Question
I believe I've heard (1-r^2) called the "coefficient of alienation," but I can't think of any references... Gaurang Mehta wrote: > I am looking for the coefficient name for (1-r^2). I know r^2 is the > Coefficient of Determination, but I do not know the name of the (1-r^2) > coefficient. > > Any assistance would be greatly appreciated. > > Thanks in advance > > GM -- Daniel Robertson College of Education Utah State University Logan, Utah
Re: could someone help me with this intro to stat. problem
On Wed, 8 Dec 1999, Mike Wogan wrote, in response to Luv 2 muah 143's question: > > 5 of 10 volunteers are randomly selected to receive self-defense > > training. The other 5 receive no training. At the end of the > > training period, all subjects complete a self-confidence > > questionnaire. > > a.) Is there a difference in self-confidence between the 2 groups > > (p<.01)? > > b.) What are the effects of self-defense traing on self-confidence > > (I'm assuming a two-tailed test?). Explain analysis > Without a pre-test measure of self-confidence, taken prior to the > training, even if there is a significant difference post-training, it's > not possible to tell whether the difference is the result of the > training or was there to begin with. Oh, come on, Mike. What did you think "randomly selected" was in there for? (Or were you trying to confuse the querent because he had the effrontery to ask a homework (or perhaps exam) question of this list?) > If there is a pre-post measurement of self-confidence, then you need a > mixed model Anova, with Training vs. No Training as the between groups > factor and Pre-Post as the within groups factor. This sure must sound scary to someone who's having trouble with the first semester of an elementary stats course! -- DFB. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-471-7128
Re: could someone help me with this intro to stat. problem
Mike Wogan writes -- - Original Message - From: Mike Wogan <[EMAIL PROTECTED]> To: Luv 2 muah 143 <[EMAIL PROTECTED]> Cc: <[EMAIL PROTECTED]> Sent: Wednesday, December 08, 1999 11:16 AM Subject: Re: could someone help me with this intro to stat. problem | On 8 Dec 1999, Luv 2 muah 143 wrote: | | > 5 of 10 volunteers are randomly selected to receive self-defense training. The | > other 5 receive no training. At the end of the training period, all subjects | > complete a self-confidence questionnaire. | > | > a.) Is there a difference in self-confidence between the 2 groups (p<.01)? | > | > | > b.) What are the effects of self-defense traing on self-confidence (I'm | > assuming a two-tailed test?). Explain analysis | > | > Please help, I can't figure it out...my mind has gone blank | | Without a pre-test measure of self-confidence, taken prior to the | training, even if there is a significant difference post-training, it's | not possible to tell whether the difference is the result of the training | or was there to begin with. | | If there is a pre-post measurement of self-confidence, then you need a | mixed model Anova, with Training vs. No Training as the between groups | factor and Pre-Post as the within groups factor. | | Mike | -- End of Mike's message -- Great suggestion, Mike -- " Without a pre-test measure of self-confidence, taken prior to the training, even if there is a significant difference post-training, it's not possible to tell whether the difference is the result of the training or was there to begin with. " The question "IN NATURAL LANGUAGE" might be stated slightly differently as: (1) For subjects who have the SAME PRE-TEST MEASURE OF SELF-CONFIDENCE but have DIFFERENT TRAINING (i.e., TRAINING vs NO-TRAINING) is their a DIFFERENCE IN THE EXPECTED POST-TEST MEASURE OF SELF-CONFIDENCE. or perhaps (2) If their is a difference between the two groups, is the difference the SAME FOR ALL VALUES OF THE PRE-TEST MEASURE OF SELF-CONFIDENCE? In these "NATURAL LANGUAGE FORMS" of the research questions the researcher should be able to write an ASSUMED MODEL that allows for the expression of the hypotheses of interest in terms OF PARAMETERS OF THE ASSUMED MODEL. Then the restrictions implied by the questions of interest can be imposed on the ASSUMED MODEL to obtain a RESTRICTED MODEL to test the hypotheses. AND NOW FOR MY "STANDARD SERMON"! The approach described as: " If there is a pre-post measurement of self-confidence, then you need a mixed model Anova, with Training vs. No Training as the between groups factor and Pre-Post as the within groups factor." DOES NOT COMMUNICATE clearly how to proceed. The reader has to learn the meaning of: "mixed model Anova" "between groups factor" "Pre-Post as within groups factor." or be able to locate a "packaged" algorithm that sounds similar to: Mixed model Anova, with Training vs. No Training as the between groups factor and Pre-Post as the within groups factor." Another "advisor" might suggest: "Do an Analysis of Covariance, with the Pre-Test Measure of Self-Confidence as the Covariable". As before, the researcher must know the meaning of the advice or locate a "package" that is labeled as "COVARIANCE ANALYSIS". This second approach is dangerous since many "packaged" COVARIANCE ANALYSIS" algorithms my not allow the researcher to answer the questions of interest, e.g. question #2 above. And even if such "packages" are located the researcher may not be able to verify that the answers produced by the "package" are related to the natural language questions of interest. In summary, statistics instruction should give students (researchers) the power to: 1.State their research questions in NATURAL LANGUAGE so that normal humans can understand. 2.Create models that allow the researcher to express hypotheses of interest. 3.Translate NATURAL LANGUAGE questions into RESTRICTIONS on parameters of the ASSUMED MODEL. 4.Impose the RESTRICTIONS to obtain a RESTRICTED MODEL. 5. Verify that the RESTRICTED MODEL has the RESTRICTIONS IMPLIED BY THE QUESTIONS OF INTEREST. 5.Use information from the ASSUMED and RESTRICTED MODELS to HELP make decisions about the questions of interest. Hopefully, (Luv 2 muah 143) is being provided the opportunity to do the above!! Reasonably talented high school students should be given to power to do this. :-) --- Joe * Joe Ward Health Careers High School 167 East Arrowhead Dr 4646 Hamilton Wolfe San Antonio, TX 78228-2402 San Antonio, TX 78229 Phone: 210-433-6575Phone: 210-617-5400 Fax: 210-433-2828 Fax: 210-617-5423 [EMAIL PROTECTED] http://www.ijoa.org/joeward/wardindex.html ***
Re: Coefficient of Determination Question
I suspect most readers (including myself) would prefer the more simple and clear terms "explained variance" and "unexplained variance." I suggest leaving the term alienation to Karl Marx's Political-Economy. Burke Johnson
Re: Coefficient of Determination Question
Hi, GM -- We always have trouble trying to give "names" to things. Usually we increase misunderstanding as we give ambiguous names to things. For example, how many folks know what is meant when they hear someone say "In a 3-factor ANOVA (A,B,C) there is a "significant 'A' MAIN EFFECT." The "someone" should just say what they really mean -- if they know! r^2 should have been "unnamed" since it's as easy to say "r square" as it is to say "coefficient of determination". However, if someone insists on giving a name to (1-r^2) then why not call it the "coefficient of non-determination". But "one minus r square" is about as easy to say as "coefficient of non-determination". -- Joe * Joe Ward Health Careers High School 167 East Arrowhead Dr 4646 Hamilton Wolfe San Antonio, TX 78228-2402 San Antonio, TX 78229 Phone: 210-433-6575 Phone: 210-617-5400 Fax: 210-433-2828 Fax: 210-617-5423 [EMAIL PROTECTED] http://www.ijoa.org/joeward/wardindex.html * - Original Message - From: Gaurang Mehta <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Wednesday, December 08, 1999 10:15 AM Subject: Coefficient of Determination Question | I am looking for the coefficient name for (1-r^2). I know r^2 is the | Coefficient of Determination, but I do not know the name of the (1-r^2) | coefficient. | | Any assistance would be greatly appreciated. | | Thanks in advance | | GM | | |
Re: Sample Distribution
Mark ( [EMAIL PROTECTED]) write: > I have a problem that puzzles me. It's a theorem that is listed in an > inference book. Here it is: > > If a random sample with size two is taken from a distribution with > positive variance and if the sum and the difference of the two > components of that sample are independent, then the distribution from > which the sample is taken is a normal distribution. > > Could anybody tell me how to proceed in order to prove that? > Feller (1971, An Introduction to Probability Theory and Its Applications, Vol II, 2 Ed.) proves a more general result in Section III.4 (pp. 77-80). Let Y1 = a X1 + b X2 and Y2 = c X1 + d X2. Feller shows: Suppose that X1 and X2 are independent of each other, and that the same is true of the pair Y1, Y2. If no coefficient a,b,c,d vanishes then all four variables are normal. gary -- [EMAIL PROTECTED] http://psych.colorado.edu Dept of Psych, CB 345, Univ of Colorado, Boulder, CO 80309-0345 USA voice: 303-492-89617 fax: 303-492-5880
Re: could someone help me with this intro to stat. problem
>On 8 Dec 1999, Luv 2 muah 143 wrote: > >> 5 of 10 volunteers are randomly selected to receive self-defense training. >> The other 5 receive no training. At the end of the training period, all >> subjects complete a self-confidence questionnaire. >> >> a.) Is there a difference in self-confidence between the 2 groups (p<.01)? >> >> b.) What are the effects of self-defense traing on self-confidence (I'm >> assuming a two-tailed test?). Explain analysis >> >> Please help, I can't figure it out...my mind has gone blank In article <[EMAIL PROTECTED]>, Mike Wogan <[EMAIL PROTECTED]> wrote: >Without a pre-test measure of self-confidence, taken prior to the >training, even if there is a significant difference post-training, it's >not possible to tell whether the difference is the result of the training >or was there to begin with. The volunteers were assigned to the two groups randomly. This design can in principal determine that the training causes a change, if other aspects are done well. In particular, with a large enough sample, the average pre-training confidence should be close to the same for the two groups. Of course, five subjects per group is rather few. Plus it's effectively a non-blinded study. Subjects are likely to think (even if not told) that self-defense training should lead to high self-confidence, making any apparent difference suspect. Administering a test to measure self-confidence beforehand would make this problem worse. Radford Neal
FW: could someone help me with this intro to stat. problem
Mike, With randomization pre, it is not necessary to take a pre-intervention measurement. Test the difference in confidence following the training. If it is significant, there is a difference. Decide what direction it is in and attribute the difference to the training. You can make this attribution because of random assignment even without pre-measure. -Original Message- From: Mike Wogan [mailto:[EMAIL PROTECTED]] Sent: Wednesday, December 08, 1999 2:16 PM To: Luv 2 muah 143 Cc: [EMAIL PROTECTED] Subject: Re: could someone help me with this intro to stat. problem On 8 Dec 1999, Luv 2 muah 143 wrote: > 5 of 10 volunteers are randomly selected to receive self-defense training. The > other 5 receive no training. At the end of the training period, all subjects > complete a self-confidence questionnaire. > > a.) Is there a difference in self-confidence between the 2 groups (p<.01)? > > > b.) What are the effects of self-defense traing on self-confidence (I'm > assuming a two-tailed test?). Explain analysis > > Please help, I can't figure it out...my mind has gone blank Without a pre-test measure of self-confidence, taken prior to the training, even if there is a significant difference post-training, it's not possible to tell whether the difference is the result of the training or was there to begin with. If there is a pre-post measurement of self-confidence, then you need a mixed model Anova, with Training vs. No Training as the between groups factor and Pre-Post as the within groups factor. Mike
Re: Disadvantage of Non-parametric vs. Parametric Test
> > Alex Yu wrote: > > > > > > Disadvantages of non-parametric tests: > > > > > > Losing precision: Edgington (1995) asserted that when more precise > > > measurements are available, it is unwise to degrade the precision by > > > transforming the measurements into ranked data. Edgington's comment is off the mark in most cases. The efficiency of the Wilcoxon-Mann-Whitney test is 3/pi (0.96) with respect to the t-test IF THE DATA ARE NORMAL. If they are non-normal, the relative efficiency of the Wilcoxon test can be arbitrarily better than the t-test. Likewise, Spearman's correlation test is quite efficient (I think the efficiency is 9/pi^2) relative to the Pearson r test if the data are bivariate normal. Where you lose efficiency with nonparametric methods is with estimation of absolute quantities, not with comparing groups or testing correlations. The sample median has efficiency of only 2/pi against the sample mean if the data are from a normal distribution. -- Frank E Harrell Jr Professor of Biostatistics and Statistics Division of Biostatistics and Epidemiology Department of Health Evaluation Sciences University of Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat
Re: could someone help me with this intro to stat. problem
- Original Message - From: Donald F. Burrill <[EMAIL PROTECTED]> To: Mike Wogan <[EMAIL PROTECTED]> Cc: Luv 2 muah 143 <[EMAIL PROTECTED]>; <[EMAIL PROTECTED]> Sent: Wednesday, December 08, 1999 12:41 PM Subject: Re: could someone help me with this intro to stat. problem | On Wed, 8 Dec 1999, Mike Wogan wrote, in response to Luv 2 muah 143's | question: | | > > 5 of 10 volunteers are randomly selected to receive self-defense | > > training. The other 5 receive no training. At the end of the | > > training period, all subjects complete a self-confidence | > > questionnaire. | | > > a.) Is there a difference in self-confidence between the 2 groups | > > (p<.01)? | | > > b.) What are the effects of self-defense traing on self-confidence | > > (I'm assuming a two-tailed test?). Explain analysis | | > Without a pre-test measure of self-confidence, taken prior to the | > training, even if there is a significant difference post-training, it's | > not possible to tell whether the difference is the result of the | > training or was there to begin with. | | Oh, come on, Mike. What did you think "randomly selected" was | in there for? (Or were you trying to confuse the querent because he | had the effrontery to ask a homework (or perhaps exam) question of this | list?) | | > If there is a pre-post measurement of self-confidence, then you need a | > mixed model Anova, with Training vs. No Training as the between groups | > factor and Pre-Post as the within groups factor. | | This sure must sound scary to someone who's having trouble with | the first semester of an elementary stats course! | -- DFB. | | Donald F. Burrill [EMAIL PROTECTED] | 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] | MSC #29, Plymouth, NH 03264 603-535-2597 | 184 Nashua Road, Bedford, NH 03110 603-471-7128 | | -- Joe Ward writes -- Hi Don, et al -- While it seems that the question is stimulated from a student's assignment, it seems to me that students should be given the "power they deserve" to do something useful when they complete their course of instruction. You indicated that-- "This sure must sound scary to someone who's having trouble with the first semester of an elementary stats course!" IT SHOULD NOT BE SCARY. If students can't "control for the uncontrollable" such as "PRE-TEST", or GENDER, etc. then they are not being given what they deserve in A NON-CALCULUS ELEMENTARY STATS COURSE. I realize that I am an "outlier" in what I believe to be a lack of SALESMANSHIP about the power that statistics can give students -- before they are "turned off". But talented high school students can do it -- so why not college students? But I get more cynical in my old age! -- Joe * Joe Ward Health Careers High School 167 East Arrowhead Dr 4646 Hamilton Wolfe San Antonio, TX 78228-2402 San Antonio, TX 78229 Phone: 210-433-6575 Phone: 210-617-5400 Fax: 210-433-2828 Fax: 210-617-5423 [EMAIL PROTECTED] http://www.ijoa.org/joeward/wardindex.html *
Re: could someone help me with this intro to stat. problem
Donald, I'm a firm believer in the effects of Maxwell's Demon. Mike
Re: Disadvantage of Non-parametric vs. Parametric Test
At 12:04 PM 12/8/99 -0500, Rich Ulrich wrote: -- snip -- >Similarly, bootstrapping is a method of "robust variance estimation" >but it does not change the metric like a power transformation does, or >abandon the metric like a rank-order transformation does. If it were >proper terminology to say randomization is nonparametric, you would >probably want to say bootstrapping is nonparametric, too. (I think >some people have done so; but it is not widespread.) In my fields of interest (ecology and evolutionary biology), it is becoming increasing common to refer to two "kinds" of bootstrapping: nonparametric bootstrapping, in which replicate samples are drawn randomly with replacement from the original sample; and parametric bootstrapping, in which samples are drawn randomly from a (usually normal) distribution having the same mean and variance as the original sample. The former is bootstrapping in the traditional sense, of course, while the latter is a form of Monte Carlo simulation. Unfortunately, the new terminology seems to be spreading rapidly. Rich Strauss Dr Richard E Strauss Biological Sciences Texas Tech University Lubbock TX 79409-3131 Email: [EMAIL PROTECTED] Phone: 806-742-2719 Fax: 806-742-2963
Re: Disadvantage of Non-parametric vs. Parametric Test
Parametric/Nonparametric bootstrap is standard terminology, used in the books by Efrom/Tibshirani, Davison/Hinkley, Chernick, Shao/Tu, and so on. It's not new, it's by now 20 years old. The parametric bootstrap is already in Efron, 1979, it's equally traditional as the nonparametric one. Both are form of MC simulation (or both are not). At 8:12 PM -0600 12/8/99, Rich Strauss wrote: >At 12:04 PM 12/8/99 -0500, Rich Ulrich wrote: > >-- snip -- > >Similarly, bootstrapping is a method of "robust variance estimation" > >but it does not change the metric like a power transformation does, or > >abandon the metric like a rank-order transformation does. If it were > >proper terminology to say randomization is nonparametric, you would > >probably want to say bootstrapping is nonparametric, too. (I think > >some people have done so; but it is not widespread.) > >In my fields of interest (ecology and evolutionary biology), it is becoming >increasing common to refer to two "kinds" of bootstrapping: nonparametric >bootstrapping, in which replicate samples are drawn randomly with >replacement from the original sample; and parametric bootstrapping, in >which samples are drawn randomly from a (usually normal) distribution >having the same mean and variance as the original sample. The former is >bootstrapping in the traditional sense, of course, while the latter is a >form of Monte Carlo simulation. Unfortunately, the new terminology seems >to be spreading rapidly. > >Rich Strauss > > > > > > >Dr Richard E Strauss >Biological Sciences >Texas Tech University >Lubbock TX 79409-3131 > >Email: [EMAIL PROTECTED] >Phone: 806-742-2719 >Fax: 806-742-2963 > === Jan de Leeuw; Professor and Chair, UCLA Department of Statistics; US mail: 8142 Math Sciences Bldg, Box 951554, Los Angeles, CA 90095-1554 phone (310)-825-9550; fax (310)-206-5658; email: [EMAIL PROTECTED] http://www.stat.ucla.edu/~deleeuw and http://home1.gte.net/datamine/ No matter where you go, there you are. --- Buckaroo Banzai
Re: Disadvantage of Non-parametric vs. Parametric Test
Robert Dawson wrote: [a long description of an instransitivity problem with WMW] This is very interesting! I'm interested to know what happens in these cases with Kruskal-Wallis - presumably it will reject. It does make the point (which I always try to make clear to people) that unless you have a shift-alternative (*or* what would be a shift-alternative after a monotonic transformation), you probably need to think about the question of interest more carefully. (i.e. what is it you're really interested in?) It often turns out in those cases that any difference in distribution is of interest, but good power against location shift is desired. This can be done without pounding WMW's square peg into that particular round hole. Glen
Re: Disadvantage of Non-parametric vs. Parametric Test
Frank E Harrell Jr wrote: > > > > Alex Yu wrote: > > > > > > > > Disadvantages of non-parametric tests: > > > > > > > > Losing precision: Edgington (1995) asserted that when more precise > > > > measurements are available, it is unwise to degrade the precision by > > > > transforming the measurements into ranked data. > > Edgington's comment is off the mark in most cases. The efficiency of the > Wilcoxon-Mann-Whitney test is 3/pi (0.96) with respect to the t-test > IF THE DATA ARE NORMAL. If they are non-normal, the relative > efficiency of the Wilcoxon test can be arbitrarily better than the t-test. > Likewise, Spearman's correlation test is quite efficient (I think the > efficiency is 9/pi^2) relative to the Pearson r test if the data are > bivariate normal. > > Where you lose efficiency with nonparametric methods is with estimation > of absolute quantities, not with comparing groups or testing correlations. > The sample median has efficiency of only 2/pi against the sample mean > if the data are from a normal distribution. Yes, the median is inefficient at the normal. This is the location estimator corresponding to the sign test in the one-sample case. But if you use the location estimator corresponding to the signed-rank test (say) instead, the efficiency improves substantially. Glen
Re: Disadvantage of Non-parametric vs. Parametric Test
Rich Ulrich wrote: > - In my vocabulary, these days, "nonparametric" starts out with data > being ranked, or otherwise being placed into categories -- it is the > infinite parameters involved in that sort of non-reversible re-scoring > which earns the label, nonparametric. (I am still trying to get my > definition to be complete and concise.) Well, I am happy for you to use this definition of nonparametric now that you've said what you want it to mean, but it isn't exactly what most statisticians - including those of us that distinguish between the terms "distribution-free" and "nonparametric" - mean by "nonparametric", so you'll have to excuse my earlier ignorance of your definition. If my recollection is correct, a parametric procedure is where the entire distribution is specified up to a finite number of parameters, whereas a nonparametric procedure is one where the distribution can't be/isn't specified with only a finite number of unspecified parameters. This typically includes the usual distribution-free procedures, including many rank-based procedures, but it also includes many other things - including some that don't transform the data in any way, and even some based on means. So, for example, ordinary simple linear regression is parametric, because the distribution of y|x is specified, up to the value of the parameters specifying the intercept and slope of the line, and the variance about the line. Nonparametric regression (as the term is typically used in the literature), by contrast, is effectively infinite-parametric, because the distribution of y|x doesn't depend only on a finite number of parameters (often the distribution *about* E[y|x] is parametric - typically gaussian - but E[y|x] itself is where the infinite-parametric part comes from). Nonparametric regression would not seem to fit your definition of "nonparametric", since your usage seems to require some loss of information through ranking or categorisation. Once we start using the same terminology, we tend to find the disagreements die down a bit. Glen
