In this paper,a theory on sieve likelihood ratio inference on general parameterspaces(including infinite dimensional)is studied.Under fairly general regularity conditions,the sieve log-likelihood ratio statistic is pr...In this paper,a theory on sieve likelihood ratio inference on general parameterspaces(including infinite dimensional)is studied.Under fairly general regularity conditions,the sieve log-likelihood ratio statistic is proved to be asymptotically X^2 distributed,whichcan be viewed as a generalization of the well-known Wilks' theorem.As an example,asemiparametric partial linear model is investigated.展开更多
基金supported in part by National Science Foundation of the USA(Grant IIS-0328802,Grant DMS-0072635)the National Natural Science Foundation of China(Grant.No.10071090 and 10231030).
文摘In this paper,a theory on sieve likelihood ratio inference on general parameterspaces(including infinite dimensional)is studied.Under fairly general regularity conditions,the sieve log-likelihood ratio statistic is proved to be asymptotically X^2 distributed,whichcan be viewed as a generalization of the well-known Wilks' theorem.As an example,asemiparametric partial linear model is investigated.
