An omnibus lack of fit test in logistic regression with sparse data

被引：3

作者：

Liu, Ying ^{[1
]}

Nelson, Paul I. ^{[2
]}

Yang, Shie-Shien ^{[2
]}

机构：

[1] Univ Missouri, Sch Dent, Kansas City, MO 64108 USA

[2] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA

来源：

STATISTICAL METHODS AND APPLICATIONS | 2012年 / 21卷 / 04期

关键词：

Model building; Binary response; Pseudo replicates; Type I error; Power; MODELS;

D O I：

10.1007/s10260-012-0197-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The usefulness of logistic regression depends to a great extent on the correct specification of the relation between a binary response and characteristics of the unit on which the response is recoded. Currently used methods for testing for misspecification (lack of fit) of a proposed logistic regression model do not perform well when a data set contains almost as many distinct covariate vectors as experimental units, a condition referred to as sparsity. A new algorithm for grouping sparse data to create pseudo replicates and using them to test for lack of fit is developed. A simulation study illustrates settings in which the new test is superior to existing ones. Analysis of a dataset consisting of the ages of menarche of Warsaw girls is also used to compare the new and existing lack of fit tests.

引用

页码：437 / 452

页数：16

共 50 条

[1] An omnibus lack of fit test in logistic regression with sparse data
Ying Liu
Paul I. Nelson
Shie-Shien Yang
[J]. Statistical Methods & Applications, 2012, 21 : 437 - 452
[2] A tree based lack-of-fit test for multiple logistic regression
Moons, E
Aerts, M
Wets, G
[J]. STATISTICS IN MEDICINE, 2004, 23 (09) : 1425 - 1438
[3] Global goodness-of-fit tests in logistic regression with sparse data
Kuss, O
[J]. STATISTICS IN MEDICINE, 2002, 21 (24) : 3789 - 3801
[4] A REGIONAL TEST FOR LACK OF FIT IN REGRESSION
UTTS, J
[J]. BIOMETRICS, 1981, 37 (04) : 852 - 852
[5] THE RAINBOW TEST FOR LACK OF FIT IN REGRESSION
UTTS, JM
[J]. COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS, 1982, 11 (24): : 2801 - 2815
[6] Sparse data and use of logistic regression
Siddarth, Prabha
[J]. EPILEPSIA, 2018, 59 (05) : 1085 - 1086
[7] A new lack-of-fit test for quantile regression with censored data
Conde-Amboage, Mercedes
Van Keilegom, Ingrid
Gonzalez-Manteiga, Wenceslao
[J]. SCANDINAVIAN JOURNAL OF STATISTICS, 2021, 48 (02) : 655 - 688
[8] Sparse logistic regression on functional data
Xu, Yunnan
Du, Pang
Robertson, John
Senger, Ryan
[J]. STATISTICS AND ITS INTERFACE, 2022, 15 (02) : 171 - 179
[9] A Bayesian Goodness of Fit Test and Semiparametric Generalization of Logistic Regression with Measurement Data
Schoergendorfer, Angela
Branscum, Adam J.
Hanson, Timothy E.
[J]. BIOMETRICS, 2013, 69 (02) : 508 - 519
[10] A lack-of-fit test for quantile regression
He, XM
Zhu, LX
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2003, 98 (464) : 1013 - 1022

← 1 2 3 4 5 →