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They also pointed out some asymptotic properties of the sequential estimation, such as asymptotic consistency, that is P ,and asymptotic efficiency, that is .

最近，A.Dmitrienko(2000)在构造极大似然估计的序贯置信区域时，提出了一个新的渐近性质：未知代价的有界性，即这是一个很值得考虑的渐近性质。

Then, followed the analysis of the asympototic properties of maximum likelihood estimation for the seasonal ARFIMA models, the consistency, efficiency and asymptotic normality of the Bayesian estimator are proved.

参照季节性ARFIMA模型的极大似然估计的渐近性质的证明思路，证明了模型参数的贝叶斯估计具有相合性、有效性和渐近正态性。

Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators.

点估计和区间估计的一般方法，估计量的小样本和大样本性质：矩法，极大似然估计，无偏估计，Rao-Blackwell 和 Lehmann-Scheffe 理论，信息不等式，渐进相对有效估计量。

Maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators.

点估计和区间估计的一般方法，估计量的小样本和大样本性质：矩法，极大似然估计，无偏估计，Rao-Blackwell 和Lehmann-Scheffe 理论，信息不等式，渐进相对有效估计量。

Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.

假设检验和经验的最有性质： Neyman-Pearson 理论，似然比率估计，score 和 Wald 经验，一致最大功效检验，局部最大功效检验，检验的渐进相对有效性。

Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability:; robustness; estimation for dependent data, nonparametric estimation and testing.

高级统计推断理论：极限理论，渐进方法，渐进有效和估计的效率，加大似然估计，贝叶斯方法，泛函数的微商渐进性，基于样本的估计：(bootstrap 和 jackknife方法)；稳健性；独立数据的估计，非参数估计和经验。

Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability:; robustness; estimation for dependent data, nonparametric

高级统计推断理论：极限理论，渐进方法，渐进有效和估计的效率，加大似然估计，贝叶斯方法，泛函数的微商渐进性，基于样本的估计：（bootstrap 和 jackknife方法）；稳健性；独立数据的估计，非参数估计和经验。

STAT 581 Advanced Theory of Statistical Inference (3) Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability:; robustness; estimation for dependent data, nonparametric estimation and testing.

高级统计推断理论：极限理论，渐进方法，渐进有效和估计的效率，加大似然估高级统计推断理论：计，贝叶斯方法，泛函数的微商渐进性，基于样本的估计：(bootstrap 和 jackknife 方法)；稳健性；独立数据的估计，非参数估计和经验。

The proposed method is based on a higher order likelihood-based asymptotic procedure. The merits of the proposed method are numerically compared with the signed log-likelihood ratio statistic and the simple t-test method with respect to their expected lengths, coverage probabilities and type I errors.

我们所利用的方法是以概似函数为基础的高阶近似方法，并且我们利用模拟的方法去跟signed log-likelihood ratio及 simple t-test 两种方法比较所建构出的信赖区间的覆盖机率和平均宽度以及所对应检定的型I错误来检视我们所利用方法的优劣。

They also show that the DSUR estimators have asymptotically mixed normal distribution and the tests of parametric restrictions can be constructed by the Wald statistic which is asymptotically distributed as a chi-square variate.

如Mark et al(2005)等提出了似无关动态协整模型的思想及其估计方法，以此来消除解释变量的内生性影响，并获得了一个标准的渐近分布。

But we don't care about Battlegrounds.

但我们并不在乎沙场中的显露。

Ah! don't mention it, the butcher's shop is a horror.

啊！不用提了。提到肉，真是糟透了。