基于目标导向的银行理财产品最优费率研究

建立基于银行目标导向的理财产品费率模型,通过对不同目标下银行行为的讨论,得出理财产品费率调整的一个分析框架。研究发现：存在使银行的经济效益和社会效益综合提升程度最大的最优理财产品费率;最优费率不是固定值,而是一个动态概念;单纯考虑经济效益时,银行将调高理财产品费率;银行调高理财产品费率,将提升自身综合目标的实现程度;银行调低理财产品费率,并不改变银行的综合目标。     

Using p values to design statistical process control charts

Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control at a given time. It is therefore not informative enough about the likelihood of a potential distributional shift. In this paper, we suggest designing the SPC charts using p values. By this approach, at each time point of Phase II process monitoring, the p value of the observed charting statistic is computed, under the assumption that the process is in-control. If the p value is less than a pre-specified significance level, then a signal of distributional shift is delivered. This p value approach has several benefits, compared to the conventional design using control limits. First, after a signal of distributional shift is delivered, we could know how strong the signal is. Second, even when the p value at a given time point is larger than the significance level, it still provides us useful information about how stable the process performs at that time point. The second benefit is especially useful when we adopt a variable sampling scheme, by which the sampling time can be longer when we have more evidence that the process runs stably, supported by a larger p value. To demonstrate the p value approach, we consider univariate process monitoring by cumulative sum control charts in various cases.     

Design-based random permutation models with auxiliary information

We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.     

An Empirical Study of Statistical Properties of Variance Partition Coefficients for Multi-Level Logistic Regression Models

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003 Goldstein , H. ( 2003 ). Multilevel Statistical Models. 3rd ed . London : Edward Arnold . [Google Scholar]) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.     

On Fan's adaptive Neyman tests for comparing two spectral densities

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.     

Comparison of One-Sided Tolerance Limits in Unbalanced One-Way Random Models

Cox's discrete logistic model was extended to the study of the life table by Thompson (1977) to handle grouped survival data. Inferences about the effect of grouping are studies byMonte Carlo methods. The results show that the effect of grouping is not substantial. This approach is applied to the grouped data on liver cancer. The computer program developed for grouped censored data with continuous and indicator covariates is of practical importance and is available fromThe Ohio State University     

Comparison of Confidence Intervals on Between Group Variance in Unbalanced Heteroscedastic One-Way Random Models

A computer algorithm for computing the alternative distributions of the Wilcoxon signed rank statistic under shift alternatives is discussed. An explicit error bound is derived for the numeric integration approximation to these distributions.

A nonparametric process control procedure in which the standard CUSUM procedure is applied to the Wilcoxon signed rank statistic is discussed. In order to implement this procedure, the distribution of the Wilcoxon statistic under shift of the underlying distribution from its point of symmetry needs to be computed. The average run length of the nonparametric and parametric CUSUM are compared.     

Positive False Discovery Rate Estimate in Step-Wise Variable Selection

Selecting predictors to optimize the outcome prediction is an important statistical method. However, it usually ignores the false positives in the selected predictors. In this article, we advocate a conventional stepwise forward variable selection method based on the predicted residual sum of squares, and develop a positive false discovery rate (pFDR) estimate for the selected predictor subset, and a local pFDR estimate to prioritize the selected predictors. This pFDR estimate takes account of the existence of non null predictors, and is proved to be asymptotically conservative. In addition, we propose two views of a variable selection process: an overall and an individual test. An interesting feature of the overall test is that its power of selecting non null predictors increases with the proportion of non null predictors among all candidate predictors. Data analysis is illustrated with an example, in which genetic and clinical predictors were selected to predict the cholesterol level change after four months of tamoxifen treatment, and pFDR was estimated. Our method's performance is evaluated through statistical simulations.     

A multiple-case deletion approach for detecting influential points in high-dimensional regression

ABSTRACT

In high-dimensional regression, the presence of influential observations may lead to inaccurate analysis results so that it is a prime and important issue to detect these unusual points before statistical regression analysis. Most of the traditional approaches are, however, based on single-case diagnostics, and they may fail due to the presence of multiple influential observations that suffer from masking effects. In this paper, an adaptive multiple-case deletion approach is proposed for detecting multiple influential observations in the presence of masking effects in high-dimensional regression. The procedure contains two stages. Firstly, we propose a multiple-case deletion technique, and obtain an approximate clean subset of the data that is presumably free of influential observations. To enhance efficiency, in the second stage, we refine the detection rule. Monte Carlo simulation studies and a real-life data analysis investigate the effective performance of the proposed procedure.     

Random sampling from the watson distribution

An envelope-rejection method is used to generate random variates from the Watson distribution. The method is compact and is competitive with, if not superior to, the existing sampling algorithms. For the girdle form of the Watson distribution, a faster algorithm is proposed. As a result, Johnson's sampling algorithm for the Bingham distribution is improved.