Large Sample Results for Likelihood-Based Methods
作者： Denni D Boos , L A Stefanski
DOI: 10.1007/978-1-4614-4818-1_6
关键词: Asymptotic distribution 、 Convergence (routing) 、 Mathematics 、 Consistency (statistics) 、 Weak consistency 、 Maximum likelihood 、 Strong consistency 、 Large sample 、 Applied mathematics 、 Value (mathematics)
摘要: Most large sample results for likelihood-based methods are related to asymptotic normality of the maximum likelihood estimator b MLE under standard regularity conditions. In this chapter we discuss these results. If consistency is assumed, then proof straightforward. Thus start with and give theorems chi-squared convergence tests TW,TS, TLR. Recall that Strong means converges probability one true value, weak refers converging in value..
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