Law of iterated logarithm and consistent model selection criterion in logistic regression

Statistics and Probability Letters

Volumn 56, Issue 1, 2002, Pages 101-112

  Qian, Guoqi ^a   Field, Chris ^b  

^a LA TROBE UNIVERSITY   (Australia)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Law of the iterated logarithm; Logistic regression; Maximum likelihood estimator; Model selection; Strong consistency

Indexed keywords

EID: 0036160757     PISSN: 01677152     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-7152(01)00191-2     Document Type: Article

References (12)

1. Information theory and an extension of the maximum likelihood principle
2. A new look at statistical model identification
3. Approaches for Bayesian variable selection
4. p
5. Limit Theorems of Probability Theory: Sequences of independent random variables
6. Some notes on Rissanen's stochastic complexity
7. Linear representation of M-estimates in linear models
8. Stochastic Complexity in Statistical Inquiry
9. Fisher information and stochastic complexity
10. Estimating the dimension of a model