Posts Tagged With: LRM

Phil 6334: Misspecification Testing: Ordering From A Full Diagnostic Menu (part 1)

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 We’re going to be discussing the philosophy of m-s testing today in our seminar, so I’m reblogging this from Feb. 2012. I’ve linked the 3 follow-ups below. Check the original posts for some good discussion. (Note visitor*)

“This is the kind of cure that kills the patient!”

is the line of Aris Spanos that I most remember from when I first heard him talk about testing assumptions of, and respecifying, statistical models in 1999.  (The patient, of course, is the statistical model.) On finishing my book, EGEK 1996, I had been keen to fill its central gaps one of which was fleshing out a crucial piece of the error-statistical framework of learning from error: How to validate the assumptions of statistical models. But the whole problem turned out to be far more philosophically—not to mention technically—challenging than I imagined. I will try (in 3 short posts) to sketch a procedure that I think puts the entire process of model validation on a sound logical footing. Continue reading

Categories: Intro MS Testing, Statistics | Tags: , , , , | 16 Comments

Misspecification Testing: (part 3) Subtracting-out effects “on paper”

Nurse chart behind her pink

A Better Way  The traditional approach described in Part 2 did not detect the presence of mean-heterogeneity and so it misidentified temporal dependence as the sole source of misspecification associated with the original LRM.

On the basis of figures 1-3 we can summarize our progress in detecting potential departures from the LRM model assumptions to probe thus far:

LRM Alternatives
(D) Distribution: Normal ?
(M) Dependence: Independent ?
(H) Heterogeneity: Identically Distributed mean-heterogeneity

Discriminating and Amplifying the Effects of Mistakes  We could correctly assess dependence if our data were ID and not obscured by the influence of the trending mean.  Although, we can not literally manipulate relevant factors, we can ‘subtract out’ the trending mean in a generic way to see what it would be like if there were no trending mean. Here are the detrended xt and yt.

 

Fig. 4: Detrended Population (y - trend )

Fig. 4: Detrended Population (y – trend )

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Categories: Intro MS Testing, Statistics | Tags: , , , | 11 Comments

Intro to Misspecification Testing: Ordering From A Full Diagnostic Menu (part 1)

 

“This is the kind of cure that kills the patient!”

is the line of Aris Spanos that I most remember from when I first heard him talk about testing assumptions of, and respecifying, statistical models in 1999.  (The patient, of course, is the statistical model.) On finishing my book, EGEK 1996, I had been keen to fill its central gaps one of which was fleshing out a crucial piece of the error-statistical framework of learning from error: How to validate the assumptions of statistical models. But the whole problem turned out to be far more philosophically—not to mention technically—challenging than I imagined.I will try (in 3 short posts) to sketch a procedure that I think puts the entire process of model validation on a sound logical footing.  Thanks to attending several of Spanos’ seminars (and his patient tutorials, for which I am very grateful), I was eventually able to reflect philosophically on aspects of  his already well-worked out approach. (Synergies with the error statistical philosophy, of which this is a part,  warrant a separate discussion.)

Continue reading

Categories: Intro MS Testing, Statistics | Tags: , , , , | 20 Comments

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