The likelihood ratio under noncontiguous alternatives

Canadian Journal of Statistics

Volumn 25, Issue 3, 1997, Pages 417-423

  Gombay, Edit ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Exponential family of distributions; Likelihood ratio test; Ornstein Uhlenbeck process; Power

Indexed keywords

EID: 0031285962     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.2307/3315788     Document Type: Article

References (12)

1. Bahadur, R.R. (1966). An optimal property of the likelihood ratio statistic. Proc. Fifth Berkeley Symp. Math. Statist. Probab. Volume 1, 13-26.
2. Barbour, A.D. (1979). On sequential versions of the generalized likelihood ratio test. Math. Proc. Cambridge Philos. Soc., 86, 85-90.
3. Brown, L.D. (1971). Non-local asymptotic optimality of appropriate likelihood ratio tests. Ann. Math. Statist., 42, 1206-1240.
4. Darling, D.A., and Erdos, P. (1956). A limit theorem for the maximum of normalized sums of independent random variables. Duke Math. J., 23, 143-145.
5. Davidson, R.R., and Lever, W.E. (1970). The limiting distribution of the likelihood ratio statistic under a class of local alternatives. Sankhyā Ser. A, 32, 209-224.
6. Efron, B., and Truax, D. (1968). Large deviations theory in exponential families. Ann. Math. Statist., 39, 1402-1424.
7. Feder, P.I. (1968). On the distribution of the log likelihood ratio test statistic when the true parameter is "near" the boundaries. Ann. Math. Statist., 39, 2044-2055.
8. Ghosh, B.K., and Sen, P.K. (1991). Handbook of Sequential Analysis. Marcel Dekker, New York.
9. Gombay, E. (1996). The weighted sequential likelihood ratio. Canad. J. Statist., 24, 229-239.
10. Serfling, R.J. (1980). Approximation Theorems of Mathematical Statistics. Wiley, New York.
11. Stroud, T.W.F. (1972). Fixed alternatives and Wald's formulation of the noncentral asymptotic behavior of the likelihood ratio statistic. Ann. Math. Statist., 43, 447-454.
12. Wald, A. (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Trans. Amer. Math. Soc., 54, 426-482.