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干扰参数仅在备择下存在时针对不等式约束的检验AI 解读

In this article we develop improved statistical tests for situations satisfying the following two nonstandard conditions simultaneously: (a) Some nuisance parameters become unidentified under the null hypothesis, and (b) the alternative hypothesis is restricted in the sense that it has inequality constraints/multiparameter one-sided hypotheses. In the statistical and econometric literature, inference problems under these two nonstandard conditions have been studied…

针对单侧备择的得分类检验AI 解读

A score-type statistic, T s , is introduced for testing H: ψ = 0 against K: ψ ≥ 0 and more general one-sided hypotheses when nuisance parameters may be present; ψ is a vector parameter. The main advantages of T s , are that it requires estimation of the model only under the null hypothesis and that, it is asymptotically equivalent to the likelihood ratio statistic; these are precisely the reasons for the popularity of the score tests for testing against two-sided…

针对单侧备择假设的得分检验AI 解读

A score-type statistic, T s , is introduced for testing H: ψ = 0 against K: ψ ≥ 0 and more general one-sided hypotheses when nuisance parameters may be present; ψ is a vector parameter. The main advantages of T s , are that it requires estimation of the model only under the null hypothesis and that, it is asymptotically equivalent to the likelihood ratio statistic; these are precisely the reasons for the popularity of the score tests for testing against two-sided…

摘要暂缺

This article considers testing for first-order moving average against first-order autoregressive disturbances in the linear-regression model. Tests investigated include approximate point-optimal invariant (POI) tests, an asymptotic test of the second-order residual autocorrelation coefficient, and a Lagragne multiplier (LM) test. A Monte Carlo experiment compares their small-sample performances. Of the asymptotic tests, the LM test has the most satisfactory sizes, but its…

线性回归模型中移动平均扰动与自回归扰动的检验AI 解读

This article considers testing for first-order moving average against first-order autoregressive disturbances in the linear-regression model. Tests investigated include approximate point-optimal invariant (POI) tests, an asymptotic test of the second-order residual autocorrelation coefficient, and a Lagragne multiplier (LM) test. A Monte Carlo experiment compares their small-sample performances. Of the asymptotic tests, the LM test has the most satisfactory sizes, but its…