Optimal Stein-type goodness-of-fit tests for count data.
Biom J
; 65(2): e2200073, 2023 02.
Article
em En
| MEDLINE
| ID: mdl-36166681
ABSTRACT
Common count distributions, such as the Poisson (binomial) distribution for unbounded (bounded) counts considered here, can be characterized by appropriate Stein identities. These identities, in turn, might be utilized to define a corresponding goodness-of-fit (GoF) test, the test statistic of which involves the computation of weighted means for a user-selected weight function f. Here, the choice of f should be done with respect to the relevant alternative scenario, as it will have great impact on the GoF-test's performance. We derive the asymptotics of both the Poisson and binomial Stein-type GoF-statistic for general count distributions (we also briefly consider the negative-binomial case), such that the asymptotic power is easily computed for arbitrary alternatives. This allows for an efficient implementation of optimal Stein tests, that is, which are most powerful within a given class F $\mathcal {F}$ of weight functions. The performance and application of the optimal Stein-type GoF-tests is investigated by simulations and several medical data examples.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
Idioma:
En
Revista:
Biom J
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha
País de publicação:
ALEMANHA
/
ALEMANIA
/
DE
/
DEUSTCHLAND
/
GERMANY