A goodness-of-fit test for parametric models based on dependently truncated data

DOI10.1016/j.csda.2011.12.022zbMath1252.62052OpenAlexW1993964106MaRDI QIDQ693227

Yoshihiko Konno, Takeshi Emura

Publication date: 7 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.12.022

zbMATH Keywords

truncationcentral limit theoremmaximum likelihoodempirical processparametric bootstrapshrinkage estimator, \texttt{R}

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Estimation in multivariate analysis (62H12) Parametric hypothesis testing (62F03) Central limit and other weak theorems (60F05) Censored data models (62N01) Bootstrap, jackknife and other resampling methods (62F40) Monte Carlo methods (65C05)

Related Items

Model diagnostic procedures for copula-based Markov chain models for statistical process control, Maximum likelihood estimation for a special exponential family under random double-truncation, Conditional maximum likelihood estimation for semiparametric transformation models with doubly truncated data, Nonparametric maximum likelihood estimation for dependent truncation data based on copulas, Parametric likelihood inference and goodness-of-fit for dependently left-truncated data, a copula-based approach, Specification tests in semiparametric transformation models --- a multiplier bootstrap approach

Uses Software

• R
• MASS (R)
• numDeriv
• bootstrap

Cites Work

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