Maximum Likelihood
Hi, Does anyone have references to a simple/intuitive introduction to Maximum Log Likelihood methods. References to algorithms would also be appreciated. Cheers, Mark W. Humphries = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Q: statistical techniques for series of events
I have a sample set of series of state-changes/events/behaviors, from this sample I'd like to generalize a scoring method for the likelihood of a criterion behavior on other data sets. Could someone guide me to the appropriate statistical technique for this type of problem and any useful resources. Thanks in advance, Mark = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
RE: Student's t vs. z tests
On 24 Apr 2001, Mark W. Humphries wrote: >> I concur. As I mentioned at the start of this thread, I am "self-learning" >> statistics from books. I have difficulty telling what is being taught as >> necessary theoretical 'scaffolding' or 'superceded procedures', and what one >> would actually apply in a realistic case. I would love a textbook which >> walks through a realistic analysis step by step, while providing the >> 'theoretical scaffolding' as insets within this flow. Its frustrating to >> read 50 pages only to find that 'one never actually does it this way'. >Jim Clark responded: >My gut feeling is that this would be a terribly confusing way to >_teach_ anything. Students would be started with a (relatively) >advanced procedure and at various points have to be taken aside >for lessons on sampling distributions, probability, whatever, and >then brought back somehow to the flow of the current lesson. >There is a logic to the way that statistics is developed in most >intro texts (although some people might not agree with that logic >in the absence of a direct empirical test of its efficacy). It >would be an interesting study of course, and not that difficult >to set up with some hypertext-like instruction. Students could >be led through the material in a hierarchical manner or entered >at some upper level with recursive links to foundational >material. We might find some kind of interaction, with better >students doing Ok by either procedure (and perhaps preferring the >latter) and weaker students doing Ok by the hierarchical >procedure but not the unstructured (for want of a better word) >method. At least, that is my prediction. [snip] You're likely right. Currently, as I learn each new concept or statistical procedure, I test my understanding by writing small snippets of code (in awk would you believe). I get perplexed when I come across descriptions which seem heuristic, rather than algorithmic. i.e. I just started the chapter on the analysis of category data. The description of the chi-squared statistic ends with "The approximation is very good provided all expected cell frequencies are 5 or greater. This is a conservative rule, and even smaller expected frequencies have resulted in good approximations." Such a statement makes me wonder if modern statistical methods actually use this particular approximation-cum-heuristic, or is there a more 'definite' algorithm. Am I learning 'real world' statistics, or a sanitized textbook version? And how can I tell? :) Cheers, Mark = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
FW: Student's t vs. z tests
-Original Message- From: Mark W. Humphries [mailto:[EMAIL PROTECTED]] Sent: Monday, April 23, 2001 12:52 PM To: dennis roberts Subject: RE: Student's t vs. z tests I concur. As I mentioned at the start of this thread, I am "self-learning" statistics from books. I have difficulty telling what is being taught as necessary theoretical 'scaffolding' or 'superceded procedures', and what one would actually apply in a realistic case. I would love a textbook which walks through a realistic analysis step by step, while providing the 'theoretical scaffolding' as insets within this flow. Its frustrating to read 50 pages only to find that 'one never actually does it this way'. Cheers, Mark -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On Behalf Of dennis roberts Sent: Friday, April 20, 2001 9:14 AM To: Alan McLean; [EMAIL PROTECTED] Subject: Re: Student's t vs. z tests At 08:46 AM 4/20/01 +1000, Alan McLean wrote: >So the two good reasons are - that the z test is the basis for the t, >and the understanding that knowledge has a very direct value. > >I hasten to add that 'knowledge' here is always understood to be >'assumed knowledge' - as it always is in statistics. > >My eight cents worth. > >Alan the problem with all these details is that ... the quality of data we get and the methods we use to get it ... PALE^2 in comparison to what such methods might tell us IF everything were clean DATA ARE NOT CLEAN! but, we prefer it seems to emphasize all this minutiae .. rather than spend much much more time on formulating clear questions to ask and, designing good ways to develop measures and collect good data every book i have seen so causally says: assume a SRS of n=40 ... when SRS are nearly impossible to get we dust off assumptions (like normality) with the flick of a cigarette ash ... we pay NO attention to whether some measure we use provides us with reliable data ... the lack of random assignment in even the simplest of experimental designs ... seems to cause barely a whimper we pound statistical significance into the ground when, it has such LIMITED application and the list goes on and on and on but yet, we get in a tizzy (me too i guess) and fight tooth and nail over such silly things as should we start the discussion of hypothesis testing for a mean with z or t? WHO CARES? ... the difference is trivial at best in the overall process of research and gathering data ... the process of analysis is the LEAST important aspect of it ... let's face it ... errors that are made in papers/articles/research projects are rarely caused by faulty analysis applications ... though sure, now and then screw ups do happen ... the biggest (by a light year) problem is bad data ... collected in a bad way ... hoping to chase answers to bad questions ... or highly overrated and/or unimportant questions NO analysis will salvage these problems ... and to worry and agonize over z or t ... and a hundred other such things is putting too much weight on the wrong things AND ALL IN ONE COURSE TOO! (as some advisors are hoping is all that their students will EVER have to take!) >-- >Alan McLean ([EMAIL PROTECTED]) >Department of Econometrics and Business Statistics >Monash University, Caulfield Campus, Melbourne >Tel: +61 03 9903 2102Fax: +61 03 9903 2007 > > >= >Instructions for joining and leaving this list and remarks about >the problem of INAPPROPRIATE MESSAGES are available at > http://jse.stat.ncsu.edu/ >= == dennis roberts, penn state university educational psychology, 8148632401 http://roberts.ed.psu.edu/users/droberts/drober~1.htm = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
RE: Student's t vs. z tests
Thank you to all who replied to my query. I now if there are other similar cases of "historical artifact" tests that have been superceded. I just purchased the book "100 statistical tests", its a bit overwhelming, I wouldn't mind witling it down to the most relevant ones. Cheers, Mark -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On Behalf Of Eric Bohlman Sent: Tuesday, April 17, 2001 4:44 AM To: [EMAIL PROTECTED] Subject: Re: Student's t vs. z tests Mark W. Humphries <[EMAIL PROTECTED]> wrote: > Hi, > I am attempting to self-study basic multivariate statistics using Kachigan's > "Statistical Analysis" (which I find excellent btw). > Perhaps someone would be kind enough to clarify a point for me: > If I understand correctly the t test, since it takes into account degrees of > freedom, is applicable whatever the sample size might be, and has no > drawbacks that I could find compared to the z test. Have I misunderstood > something? You're running into a historical artifact: in pre-computer days, using the normal distribution rather than the t distribution reduced the size of the tables you had to work with. Nowadays, a computer can compute a t probability just as easily as a z probability, so unless you're in the rare situation Karl mentioned, there's no reason not to use a t test. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Student's t vs. z tests
Hi, I am attempting to self-study basic multivariate statistics using Kachigan's "Statistical Analysis" (which I find excellent btw). Perhaps someone would be kind enough to clarify a point for me: If I understand correctly the t test, since it takes into account degrees of freedom, is applicable whatever the sample size might be, and has no drawbacks that I could find compared to the z test. Have I misunderstood something? Thanks in advance for your help. Cheers, Mark = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
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