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A goodness-of-fit test for logistic ...

Xie, Xianjin.

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A goodness-of-fit test for logistic regression models with continuous predictors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A goodness-of-fit test for logistic regression models with continuous predictors./
Author:	Xie, Xianjin.
Description:	182 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4308.
Contained By:	Dissertation Abstracts International66-08B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3184769
ISBN:	9780542263781

A goodness-of-fit test for logistic regression models with continuous predictors.
Xie, Xianjin.

A goodness-of-fit test for logistic regression models with continuous predictors. - 182 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4308.

Thesis (Ph.D.)--The University of Iowa, 2005.

When continuous predictors are present, classical Pearson and deviance goodness-of-fit tests to assess logistic model fit break down. The Hosmer-Lemeshow goodness-of-fit statistic is often used in these situations. Their procedure groups observations into G bins (usually 10) according to the percentiles of the estimated probabilities. It uses a Pearson chi-square statistic with G-2 degrees of freedom to compare the observed frequency of events to that expected using the model's average predicted value in each bin. While simple to perform with relatively satisfactory properties, it provides no further information on the source of any detectable lack of fit. Tsiatis (1980) proposed an alternative statistic based on a partitioning of the covariate space and used a score statistic to test for regional effects being zero. While conceptually elegant, its lack of a general rule for how to partition the covariate space has, to a certain degree, limited its usefulness and popularity.

ISBN: 9780542263781Subjects--Topical Terms:

517247
Statistics.

A goodness-of-fit test for logistic regression models with continuous predictors.
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245 1 2 $a A goodness-of-fit test for logistic regression models with continuous predictors.
300 $a 182 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4308.
500 $a Supervisors: Jane F. Pendergast; William R. Clarke.
502 $a Thesis (Ph.D.)--The University of Iowa, 2005.
520 $a When continuous predictors are present, classical Pearson and deviance goodness-of-fit tests to assess logistic model fit break down. The Hosmer-Lemeshow goodness-of-fit statistic is often used in these situations. Their procedure groups observations into G bins (usually 10) according to the percentiles of the estimated probabilities. It uses a Pearson chi-square statistic with G-2 degrees of freedom to compare the observed frequency of events to that expected using the model's average predicted value in each bin. While simple to perform with relatively satisfactory properties, it provides no further information on the source of any detectable lack of fit. Tsiatis (1980) proposed an alternative statistic based on a partitioning of the covariate space and used a score statistic to test for regional effects being zero. While conceptually elegant, its lack of a general rule for how to partition the covariate space has, to a certain degree, limited its usefulness and popularity.
520 $a We propose a new method for goodness-of-fit testing which uses a very general partitioning strategy (clustering) in the covariate space and is based on either a Pearson statistic or a score statistic. Properties of the proposed statistics are discussed. Simulation studies on many commonly encountered model scenarios are presented to compare the proposed tests to the existing tests. Applications of these different methods on a real clinical trial study are also performed to demonstrate the usefulness of the new method in practice and certain advantages over the widely used Hosmer-Lemeshow test. Discussions on extending this new method to other data situations, such as ordinal response regression models and marginal models for correlated binary data are also included. This method can also be extended to models for multinomial outcomes where generalized logit models are often used.
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