Inferences on the reliability in balanced and unbalanced one-way random models

In this article, the hypothesis testing and interval estimation for the reliability parameter are considered in balanced and unbalanced one-way random models. The tests and confidence intervals for the reliability parameter are developed using the concepts of generalized p-value and generalized confidence interval. Furthermore, some simulation results are presented to compare the performances between the proposed approach and the existing approach. For balanced models, the simulation results indicate that the proposed approach can provide satisfactory coverage probabilities and performs better than the existing approaches across the wide array of scenarios, especially for small sample sizes. For unbalanced models, the simulation results show that the two proposed approaches perform more satisfactorily than the existing approach in most cases. Finally, the proposed approaches are illustrated using two real examples.     

Testing for Smooth Structural Changes in Time Series Models via Nonparametric Regression

Checking parameter stability of econometric models is a long‐standing problem. Almost all existing structural change tests in econometrics are designed to detect abrupt breaks. Little attention has been paid to smooth structural changes, which may be more realistic in economics. We propose a consistent test for smooth structural changes as well as abrupt structural breaks with known or unknown change points. The idea is to estimate smooth time‐varying parameters by local smoothing and compare the fitted values of the restricted constant parameter model and the unrestricted time‐varying parameter model. The test is asymptotically pivotal and does not require prior information about the alternative. A simulation study highlights the merits of the proposed test relative to a variety of popular tests for structural changes. In an application, we strongly reject the stability of univariate and multivariate stock return prediction models in the postwar and post‐oil‐shocks periods.     

基于变位置参数贝叶斯预测银行内部欺诈研究

内部欺诈事件类型是中国商业银行最严重的操作风险类型。但由于操作风险本质特征和中国商业银行内部欺诈损失数据收集年度较短,数据匮乏,为了在小样本数据下进行更准确的度量,本文采用贝叶斯后验预测分布方法,其中,假设损失频率服从泊松-伽马分布,而损失强度服从广义帕累托-混合伽马分布,分析后验分布的形式。由于在广义帕累托分布的参数估计中,位置参数的确定对估计结果的影响很大,因此,本文采用变位置参数线性趋势的贝叶斯分析以增强参数预测稳定性,降低位置参数选择对结果产生的影响,获得中国商业银行内部欺诈损失频率和损失强度的后验预测分布和边际分布,进而采用蒙特卡罗模拟,联合损失频率分布和损失强度的预测分布获得内部欺诈的风险联合分布。与传统Poisson-GPD极值分析法相比,在险值和预期超额损失明显降低,有利于银行降低内部欺诈操作风险资本。利用贝叶斯分析获得的后验分布可以作为未来的先验分布,有利于在较小样本下获得较真实的参数估计。     

基于有理样条死亡假设的分数时点寿险净保费责任准备金

保险责任准备金是保险公司风险管理的重要度量指标,责任准备金的精确合理的测算,将会对保险公司的健康发展起着极其重要的作用。分数时点净保费责任准备金的测算依赖于精算假设,本文在提出一类有理样条死亡假设的基础上,研究了终身寿险的分数时点净保费责任准备金的计算问题。我们得到了其理论计算公式和上下界范围,探讨了调节参数的变化对净保费责任准备金的影响。数据分析表明:分数时点责任准备金对调节参数的变化比较敏感,目前常用的UDD假设下的责任准备金测算值恰是本文方法下的一个边界。所以基于有理样条估计方法的分数时点责任准备金测算在实务中具有很强的灵活性,对保险公司责任准备金风险管理具有重要的指导意义。     

On finite-sample properties of adaptive least squares regression estimates

This paper is mainly concerned with deriving finite-sample properties of least squares estimators for the regression function in a nonparametric regression situation under some simplifying assumptions such as normally distributed errors with a common known variance. The selection of basis functions to be used for the construction of an estimator may be regarded as a smoothing problem, and will usually be done in a data-dependent way, A straightforward application of a result by P. J. Kernpthorne yields that, under a squared error loss, all selection procedures are admissible. Furthermore, the minimax approach provides an interpolating estimator, which is often impractical, Therefore, within a certain class of selection procedures an optimal one is determined using the minimax regret principle. It can be seen to behave similarly to the procedure minimizing either an unbiased risk estimator or, equivalently, the C_p-criterion.     

Point estimation of a global optimum for large combinatorial problems

The basis for this paper is in the following observation: for a given “ intractable” optimization problem for which no efficient solution technique exists, if we can devise a systematic procedure for generating independent, heuristic solutions, we should be able to apply statistical extreme-value theory in order to obtain point estimates for the globally optimal solution. This observation has been mechanized in order to evaluate heuristic solutions and assess deviations from optimality, the strategy developed is applicable to a host of combinatorial problems. The assumptions of our model, along with computational experience are discussed.     

A simulation study of the stochastic ridge k

A simulation of regressions is used to generate estimates by iteration of the generalized ridge parameter.The simulation results indicate that generalized ridge parameters estimated by iteration from near collinear data may be quite different from unknown optimal values.     

Kshirsagar-Type Lower Bounds for Mean Squared Error of Prediction

Let Y be an observable random vector and Z be an unobserved random variable with joint density f(y, z | θ), where θ is an unknown parameter vector. Considering the problem of predicting Z based on Y, we derive Kshirsagar type lower bounds for the mean squared error of any predictor of Z. These bounds do not require the regularity conditions of Bhattacharyya bounds and hence are more widely applicable. Moreover, the new bounds are shown to be sharper than the corresponding Bhattacharyya bounds. The conditions for attaining the new lower bounds are useful for easy derivation of best unbiased predictors, which we illustrate with some examples.