Improved transformed deviance statistic for testing a logistic regression model
From MaRDI portal
Publication:549919
DOI10.1016/j.jmva.2011.04.010zbMath1217.62100OpenAlexW1990403945MaRDI QIDQ549919
Nobuhiro Taneichi, Jun Toyama, Yuri Sekiya
Publication date: 19 July 2011
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2115/47351
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Asymptotic approximations for the distributions of the multinomial goodness-of-fit statistics under local alternatives
- Closer asymptotic approximations for the distributions of the power divergence goodness-of-fit statistics
- Asymptotic approximations for the distributions of multinomial goodness- of-fit statistics
- Improvement of approximations for the distributions of multinomial goodness-of-fit statistics under nonlocal alternatives
- Transformations with improved chi-squared approximations
- Improved transformed statistics for the test of independence in \(r\times s\) contingency tables
- A GENERAL METHOD FOR APPROXIMATING TO THE DISTRIBUTION OF LIKELIHOOD RATIO CRITERIA
- Approximations of the Distributions of Test Statistics for Homogeneity of a Product Multinomial Model
- On the level-error after Bartlett adjustment of the likelihood ratio statistic
- Asymptotic approximations for the distributions of the (h‐, φ‐)‐divergence goodness‐of‐fit statistics: application to Renyi’s statistic
- A Method for Improving the Large-Sample Chi-Squared Approximations to Some Multivariate Test Statistics
- AN ASYMPTOTIC APPROXIMATION FOR THE DISTRIBUTION OF ^|^phi;-DIVERGENCE MULTINOMIAL GOODNESS-OF-FIT STATISTIC UNDER LOCAL ALTERNATIVES
- Asymptotic Approximations for the Probability that a Sum of Lattice Random Vectors Lies in a Convex Set
This page was built for publication: Improved transformed deviance statistic for testing a logistic regression model
