新型农村合作医疗参合农民满意度影响因素辨析及其政策优化:一个基于L市的案例分析

参合农民的满意度是评价新农合基金绩效的重要指标。对L市2015年592位参合农民的样本数据进行分析,发现就医过程对参合农民满意度影响最为显著,其中,农民满意度与新农合定点医疗机构的医疗水平和综合费用有很强的相关度;新农合基金管理水平对参合农民满意度有较为显著的影响,制度设计对参合农民满意度影响最小。因此,为优化新农合基金政策绩效、提升参合农民满意度水平,应构建高质量医疗服务供给机制、强化农民主动参与机制、推进新农合政府管理机构能力建设以及对新农合基金绩效开展持续评价。     

家族控制程度对上市公司股价情绪效应的影响研究

上市公司家族控制程度的差异是否会引起投资者对其股票估值的不确定性的变化,这一问题在现有家族企业研究文献中鲜有涉及。基于我国A股家族上市公司样本的2009—2017年度数据,从股票超额收益率对投资者情绪敏感度的角度,分析了家族控制程度对股价情绪效应的影响。研究发现,家族控制程度的增加会加剧上市公司股价的情绪效应,股票超额收益率对投资者情绪的波动更为敏感,而且公司规模越小,账面市值比越低,则其股价情绪效应越明显。股权制衡程度更高的公司,家族控制程度对其股价情绪效应的影响会降低,说明有效的治理结构会减缓股价情绪效应。公司主动性信息披露、代际传承进展,以及公司所处行业的市场竞争程度,均显著地负向调节家族控制程度对股价情绪效应的影响,减缓了收益率对情绪的敏感程度。通过改善这些变量的监管状况,可以有效缓解投资者情绪对家族控制公司股价的冲击,促进股票市场的平稳运行。     

Stochastic Aging Classes for the Maximum Statistic from Friday and Patil Bivariate Exponential Distribution Family

Friday and Patil bivariate exponential (FPBVE) distribution family is one of the most flexible bivariate exponential distributions in the literature; among others, it contains the bivariate exponential models due to Freund, Marshall–Olkin, Block–Basu, and Proschan–Sullo as particular cases. In this article, we discuss the stochastic aging of the maximum statistic from FPBVE model in according to the log-concavity of its density function, i.e., in the increasing or decreasing likelihood ratio classes (ILR or DLR), and consequently in the IFR and DFR classes. Furthermore, a kind of DFR distributions which are not DLR is derived from our classification.     

Use of Spacings in the Estimation of Common Scale Parameter of Several Distributions

In this article, we consider the problem of best linear unbiased estimation and best linear invariant estimation of the common scale parameter of several distributions using spacing of the pooled sample of all observations of individual samples. We derived conditions for the non negativity of the scale estimator obtained by the above methods. Further, we obtained necessary and sufficient conditions for the derived estimators to be constant multiples of the pooled sample range.     

A Weighting Approach for GEE Analysis with Missing Data

We propose a new weighting (WT) method to handle missing categorical outcomes in longitudinal data analysis using generalized estimating equations (GEE). The proposed WT provides a valid GEE estimator when the data are missing at random (MAR), and has more stable weights and shows advantage in efficiency compared to the inverse probability weighing method in the presence of small observation probabilities. The WT estimator is similar to the stabilized weighting (SWT) estimator under mild conditions, but it is more stable and efficient than SWT when the associations of the outcome with the observation probabilities and the covariate are strong.     

Relations for Moments of Progressively Type-II Censored Order Statistics from Log-Logistic Distribution with Applications to Inference

In this article, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a log-logistic distribution. The use of these relations in a systematic recursive manner would enable the computation of all the means, variances and covariances of progressively Type-II right censored order statistics from the log-logistic distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R ₁,…, R _m ). The results established here generalize the corresponding results for the usual order statistics due to Balakrishnan and Malik (1987 Balakrishnan , N. , Malik , H. J. ( 1987 ). Moments of order statistics from truncated log-logistic distribution . J. Statist. Plann. Infer. 17 : 251 – 267 .[Crossref], [Web of Science ®] , [Google Scholar]) and Balakrishnan et al. (1987 Balakrishnan , N. , Malik , H. J. , Puthenpura , S. ( 1987 ). Best linear unbiased estimation of location and scale parameters of the log-logistic distribution . Commun. Statist. Theor. Meth. 16 : 3477 – 3495 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The moments so determined are then utilized to derive best linear unbiased estimators for the scale- and location-scale log-logistic distributions. A comparison of these estimates with the maximum likelihood estimates is made through Monte Carlo simulation. The best linear unbiased predictors of progressively censored failure times is then discussed briefly. Finally, a numerical example is presented to illustrate all the methods of inference developed here.     

Use of Quasi-Ranges in the Estimation of Scale Parameter of a Symmetric Distribution

In this article, we consider the problem of best linear unbiased estimation and best linear invariant estimation of the scale parameter of a symmetric distribution using quasi-ranges is considered. We also prove a sufficient condition for the non negativity of the scale estimator obtained by the above method. Further, we obtain necessary and sufficient conditions for the derived estimators to be constant multiple of the sample range.     

The Recursive Kernel Distribution Function Estimator Based on Negatively and Positively Associated Sequences

Let {X _j , j ≥ 1} be a strictly stationary negatively or positively associated sequence of real valued random variables with unknown distribution function F(x). On the basis of the random variables {X _j , j ≥ 1}, we propose a smooth recursive kernel-type estimate of F(x), and study asymptotic bias, quadratic-mean consistency and asymptotic normality of the recursive kernel-type estimator under suitable conditions.     

Normal Deviation and Poisson Approximation of a Security Market by the Geometric Markov Renewal Processes

We consider the geometric Markov renewal processes (GMRP) as a model for a security market. Normal deviations of the geometric Markov renewal processes for ergodic averaging and double averaging schemes are derived. We introduce Poisson averaging scheme for the geometric Markov renewal processes. European call option pricing formulas for GMRP are presented.     

General Multivariate Weibull Processes

Two multivariate stationary processes with general multivariate Weibull marginals are developed and studied. The joint distribution of the two adjacent events in the processes and the distributions of the finite sample minima as well as the geometric minima are derived. The characterization properties of these two processes are also proved.