Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data.
Stat Med
; 38(22): 4310-4322, 2019 09 30.
Article
em En
| MEDLINE
| ID: mdl-31317564
ABSTRACT
Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Sobrevida
/
Análise de Regressão
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
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Prognostic_studies
Limite:
Humans
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Male
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Canadá