Asymptotic Properties of Random Weighted Empirical Distribution Function

This article studies the asymptotic properties of the random weighted empirical distribution function of independent random variables. Suppose X₁, X₂, ???, X_n is a sequence of independent random variables, and this sequence is not required to be identically distributed. Denote the empirical distribution function of the sequence by F_n(x). Based on the random weighting method and F_n(x), the random weighted empirical distribution function H_n(x) is constructed and the asymptotic properties of H_n are discussed. Under weak conditions, the Glivenko–Cantelli theorem and the central limit theorem for the random weighted empirical distribution function are obtained. The obtained results have also been applied to study the distribution functions of random errors of multiple sensors.     

Asymptotic Representations of the Multivariate Empirical Distribution Function and Applications

Often, many complicated statistics used as estimators or test statistics take the form of the (multivariate) empirical distribution function evaluated at a random vector (V_n). Denote such statistics by S_n. This paper describes methods for the study of various asymptotic properties of S_n. First, under minimal assumptions, a weak asymptotic representation for S_n is derived. This result may be used to show the asymptotic normality of S_n. Second, under slightly more stringent regularity conditions, an almost sure representation of S_n, with suitable order (as.) of the remainder term is studied and then a law of the iterated logarithm for S_n, is derived. In this context, strong convergence results from a sequential point of view are also studied. Finally, weak convergence to a Brownian motion process is established. As an application, we show the limiting normality of S_n, for a random number of summands.     

Random Weighting Empirical Distribution Function and its Applications to Goodness-of-Fit Testing

In this article, a randomized estimator of the empirical distribution function (EDF) called random weighting empirical distribution function (RWEDF) is introduced, one special case of which is just equivalent to the Bayesian bootstrap. The consistency of the RWEDF is established under certain conditions. By substituting this new EDF for the classical EDF, we obtain new versions of some EDF test statistics for goodness-of-fit. The simulation results show that the new tests are more powerful than the corresponding tests based on the classical EDF under some cases.     

Testing Chaos Based on Empirical Distribution Function: A Simulation Study

It is well known that many classical statistical tests of randomness generally fail to distinguish chaos generated by some lower-dimensional deterministic dynamical systems from independent and identically distributed (i.i.d.) random series. In this paper, we suggest a powerful statistical testing method based on empirical distribution function that can well detect chaos and i.i.d. random series.     

上市公司业绩评价模型与实证分析、

近年来,业绩评价受到越来越多的关注。在目前理论界上市公司业绩评价指标体系的基础上,本文引偏最小二乘回归方法进行实证分析,发现可以得到较好的预测上市公司未来中长期的业绩的模型。     

利用稳定分布构建开放式基金业绩评价体系

经典的基金业绩评价方法是在收益率服从正态分布假设下构建风险指标，该指标或者高估风险，或者低估风险。可引入稳定分布来描述收益率，并利用稳定分布，构建风险调整的基金业绩评价体系，并利用该体系对我国开放式基金的业绩进行评价。     

Empirical Comparison of Nonparametric Regression Estimates on Real Data

The performance of nine different nonparametric regression estimates is empirically compared on ten different real datasets. The number of data points in the real datasets varies between 7, 900 and 18, 000, where each real dataset contains between 5 and 20 variables. The nonparametric regression estimates include kernel, partitioning, nearest neighbor, additive spline, neural network, penalized smoothing splines, local linear kernel, regression trees, and random forests estimates. The main result is a table containing the empirical L₂ risks of all nine nonparametric regression estimates on the evaluation part of the different datasets. The neural networks and random forests are the two estimates performing best. The datasets are publicly available, so that any new regression estimate can be easily compared with all nine estimates considered in this article by just applying it to the publicly available data and by computing its empirical L₂ risks on the evaluation part of the datasets.     

基于CCA—DEA模型评估政府社会保障绩效的实证研究

衡量和评价由社会保险、社会救助、社会福利和社会优抚构成的社会保障的运行状况和绩效水平,需要运用科学、合理的评估方法。目前,对社会保障绩效评估的方法相对薄弱。鉴于此,运用典型相关分析与数据包络分析方法（CCA—DEA）相结合评价中国社会保障绩效,从定量分析的角度得出中国不同省份在2005—2007年份社会保障绩效的相对大小,结果表明：有80％的地区社会保障部门在养老保险、医疗保险、失业保险、工伤保险、生育保险和社会福利等方面是DEA有效,另外20％左右的地区仍需要提高。     

基于DEA模型评估江西省五市政府绩效实证研究

地区政府绩效评估自80年代以来日益受到重视,其评估指标体系不断完善,评估手段也日趋多元化。在以往的地方政府绩效相对有效性的评估中,往往很难兼顾评估的综合性和客观性。鉴于此,文章尝试应用DEA模型,在2004年8月国家人事部《中国地方政府绩效评估》课题组提出的33评估体系的基础上,选用代表性指标,对江西省人均GDP位居前五位的南昌、九江等五个市的政府绩效进行评估,得出一些结论并提供若干政策建议,从而进一步说明DEA模型评估政府绩效的有效性及适用范围。     

The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models

Abstract.  In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M -estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the 'Aspect problem' in quantum physics.     

A Study of Blockwise Wavelet Estimates Via Lower Bounds for a Spike Function

Abstract.  A blockwise shrinkage is a popular adaptive procedure for non-parametric series estimates. It possesses an impressive range of asymptotic properties, and there is a vast pool of blocks and shrinkage procedures used. Traditionally these estimates are studied via upper bounds on their risks. This article suggests the study of these adaptive estimates via non-asymptotic lower bounds established for a spike underlying function that plays a pivotal role in the wavelet and minimax statistics. While upper-bound inequalities help the statistician to find sufficient conditions for a desirable estimation, the non-asymptotic lower bounds yield necessary conditions and shed a new light on the popular method of adaptation. The suggested method complements and knits together two traditional techniques used in the analysis of adaptive estimates: a numerical study and an asymptotic minimax inference.     

Local Smoothing for Kernel Distribution Function Estimation

The problem of bandwidth selection for kernel-based estimation of the distribution function (cdf) at a given point is considered. With appropriate bandwidth, a kernel-based estimator (kdf) is known to outperform the empirical distribution function. However, such a bandwidth is unknown in practice. In pointwise estimation, the appropriate bandwidth depends on the point where the function is estimated. The existing smoothing methods use one common bandwidth to estimate the cdf. The accuracy of the resulting estimates varies substantially depending on the cdf and the point where it is estimated. We propose to select bandwidth by minimizing a bootstrap estimator of the MSE of the kdf. The resulting estimator performs reliably, irrespective of where the cdf is estimated. It is shown to be consistent under i.i.d. as well as strongly mixing dependence assumption. Two applications of the proposed estimator are shown in finance and seismology. We report a dataset on the S & P Nifty index values.     

Adjusted Estimates of the Size Distribution of Family Money Income

This article describes the effects on estimates of the size distribution of family-unit money income produced by adjusting CPS estimates for 1972 by adding several other data sources. Income estimates were adjusted on an individual-observation basis to make them consistent with independent control totals. As a result of these adjustments, mean income for all units rose 12 percent. The relative share of the top 5 percent increased substantially. Property income increased and wage income decreased in relative importance. The adjustment to mean income was largest for the oldest age group and smallest for the youngest age group.     

An Empirical Bayes Inference for the von Mises Distribution

This paper develops an empirical Bayesian analysis for the von Mises distribution, which is the most useful distribution for statistical inference of angular data. A two-stage informative prior is proposed, in which the hyperparameter is obtained from the data in one of the stages. This empirical or approximate Bayes inference is justified on the basis of maximum entropy, and it eliminates the modified Bessel functions. An example with real data and a realistic prior distribution for the regression coefficients is considered via a Metropolis-within-Gibbs algorithm.     

Inference for the Mean Residual Life Function via Empirical Likelihood

In addition to the distribution function, the mean residual life (MRL) function is the other important function which can be used to characterize a lifetime in survival analysis and reliability. For inference on the MRL function, some procedures have been proposed in the literature. However, the coverage accuracy of such procedures may be low when the sample size is small. In this article, an empirical likelihood (EL) inference procedure of MRL function is proposed and the limiting distribution of the EL ratio for MRL function is derived. Based on the result, we obtain confidence interval/band for the MRL function. The proposed method is compared with the normal approximation based method through simulation study in terms of coverage probability.     

消费者对移动支付服务商的信任维度实证研究

移动支付作为关键技术环节制约着移动商务的发展,而消费者对移动支付服务商的信任是影响消费者是否采纳移动支付的主要障碍。首先,通过文献回顾形成消费者移动支付信任构成初始量表,然后对在线收集的425份样本进行主成分因子分析,萃取出能力、正直和善意三个信任维度,累计解释了76.085%的方差,信度检验显示三个信任维度Cronbach'sα值均大于0.8,一阶和二阶验证性因子分析表明量表建构效度理想。研究结果表明,三个维度中正直信任对消费者总体信任水平影响程度最大,其次分别是善意信任和能力信任。     

Performance of Interval Estimators for the Inverse Hypergeometric Distribution

The inverse hypergeometric distribution is of interest in applications of inverse sampling without replacement from a finite population where a binary observation is made on each sampling unit. Thus, sampling is performed by randomly choosing units sequentially one at a time until a specified number of one of the two types is selected for the sample. Assuming the total number of units in the population is known but the number of each type is not, we consider the problem of estimating this parameter. We use the Delta method to develop approximations for the variance of three parameter estimators. We then propose three large sample confidence intervals for the parameter. Based on these results, we selected a sampling of parameter values for the inverse hypergeometric distribution to empirically investigate performance of these estimators. We evaluate their performance in terms of expected probability of parameter coverage and confidence interval length calculated as means of possible outcomes weighted by the appropriate outcome probabilities for each parameter value considered. The unbiased estimator of the parameter is the preferred estimator relative to the maximum likelihood estimator and an estimator based on a negative binomial approximation, as evidenced by empirical estimates of closeness to the true parameter value. Confidence intervals based on the unbiased estimator tend to be shorter than the two competitors because of its relatively small variance but at a slight cost in terms of coverage probability.     

Modification of Pickands' Dependence Function for Ordered Bivariate Extreme Distribution

We study the characteristics of the Pickands' dependence function for bivariate extreme distribution for minima, BEVM, when considering the stochastics ordering of the two variables, X < Y. The existing Pickand's dependence function terminologies and theories are modified to suit the dependence functions of extreme minimum cases. The main result is the introduction of the restricted logistic dependence function, A _RL , and the restricted exponential function, V _RL (x, y).