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剖面似然的稳健调整AI 解读

Under mild misspecifications of model assumptions, maximum likelihood estimates often remain consistent and asymptotically normal. Asymptotic normality will often hold for the signed root of the likelihood ratio statistic and the score statistic as well. However, standard estimates of asymptotic variance are usually inconsistent. This occurs when Bartlett's second identity fails. In the manner of McCullagh and Tibshirani, a variance correction may be used to adjust the…

研究剖面似然调整的符号算法AI 解读

A symbolic algorithm for the derivation of the asymptotic expansion of the maximum likelihood estimate was first introduced by Andrews & Stafford (1993). This algorithm is quite general and can be modified to give asymptotic expansions for the maximum likelihood estimate based on either the conditional, modified or adjusted profile likelihoods. Expansions are obtained quickly with no chance for human clerical error. As a result, these three adjustments can be easily studied…

渐近展开的符号计算工具AI 解读

SUMMARY This paper describes a collection of procedures for the systematic computation of asymptotic expansions that are common in statistical theory and practice: expansions of functions of sums of independent and identically distributed random variables. The procedures permit the expansion of maximum likelihood estimates, the associated deviance or drop in likelihood and more general functions of random variables with distributions involving one or more parameters. The…

研究剖面似然调整的符号算法AI 解读

A symbolic algorithm for the derivation of the asymptotic expansion of the maximum likelihood estimate was first introduced by Andrews & Stafford (1993). This algorithm is quite general and can be modified to give asymptotic expansions for the maximum likelihood estimate based on either the conditional, modified or adjusted profile likelihoods. Expansions are obtained quickly with no chance for human clerical error. As a result, these three adjustments can be easily studied…