Modeling censored data using modified lambda family

In the present article we propose the modified lambda family (MLF) which is the Freimer, Mudholkar, Kollia, and Lin (FMKL) parametrization of generalized lambda distribution (GLD) as a model for censored data. The expressions for probability weighted moments of MLF are derived and used to estimate the parameters of the distribution. We modified the estimation technique using probability weighted moments. It is shown that the distribution provides reasonable fit to a real censored data.     

A Proportional Hazards Regression Model for the Subdistribution with Covariates‐adjusted Censoring Weight for Competing Risks Data

With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate‐dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate‐dependent censoring. We consider a covariate‐adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate‐adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate‐adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research. Here, cancer relapse and death in complete remission are two competing risks.     

Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation

Inverse probability weighting (IPW) and multiple imputation are two widely adopted approaches dealing with missing data. The former models the selection probability, and the latter models data distribution. Consistent estimation requires correct specification of corresponding models. Although the augmented IPW method provides an extra layer of protection on consistency, it is usually not sufficient in practice as the true data‐generating process is unknown. This paper proposes a method combining the two approaches in the same spirit of calibration in sampling survey literature. Multiple models for both the selection probability and data distribution can be simultaneously accounted for, and the resulting estimator is consistent if any model is correctly specified. The proposed method is within the framework of estimating equations and is general enough to cover regression analysis with missing outcomes and/or missing covariates. Results on both theoretical and numerical investigation are provided.     

Inference on a progressive type I interval-censored truncated normal distribution

In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.     

Toward an integrated Bayesian network approach to measurement error detection and correction

In this article, the quality of data produced by national statistical institutes and by governmental institutions is considered. In particular, the problem of measurement error is analyzed and an integrated Bayesian network decision support system based on non-parametric Bayesian networks is proposed for its detection and correction. Non-parametric Bayesian networks are graphical models expressing dependence structure via bivariate copulas associated to the edges of the graph. The network structure and the misreport probability are estimated using a validation sample. The Bayesian network model is proposed to decide: (i) which records have to be corrected; (ii) the kind and amount of correction to be adopted. The proposed correction procedure is applied to the Banca d’Italia Survey on Household Income and Wealth and, specifically, the bond amounts are analyzed. Finally, the sensitivity of the conditional distribution of the true value random variable given the observed one to different evidence configurations is studied.     

An improved cluster two-stage randomized response model

In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement. The extended models result in more efficient estimators than Lee et al.'s (2014) estimators of the proportion of the population having a sensitive attribute.     

网络侵权投诉的平台审核及边界

现行法律规定网络平台侵权投诉（即通知）的形式审查,在机制目的设置上,旨在降低平台实质审查带来的高成本。然而,过于简单的形式审查要件之规定对平台的稳定性与人力审核成本反而提出了更高的要求。为维护平台信息数据的稳定性,平台难免会选择实质审查来减少形式审查所产生的信息波动。这又与网络平台侵权投诉形式审查的宗旨相悖。平台针对不同的权利类型的投诉进行审核,其中最为核心的就是对“构成侵权的初步证明材料”的把握。这个过程是个主观大于客观的自我内心确信。在以高度盖然性为标准的大前提下,网络平台要做的是权利归属的恰当性判断。     

单窃听双跳协作网络的中继选择方案及其性能分析

研究了安全通信意义下,单向译码转发（decode-and-forward,DF）协作无线网络的中继选择问题。针对窃听者既能获得信源发出的信号,又能窃取中继节点转发数据的通信系统,提出了3种中继选择方案来对抗窃听者,增强系统物理层安全性。其中,方案一选择到窃听者信噪比（signal-to-noise ratio,SNR）最小的中继节点;方案二为最大最小（max-min）选择方案,即选择信源到中继节点和中继节点到信宿的较差信噪比中最大值所对应的中继节点;方案三根据窃听信道和主信道的瞬时信道状态信息（channel state information,CSI）选择使得窃听网络有最大保密容量的中继节点。在对各方案的性能分析过程中,得到了各中继选择方案拦截概率的闭式表示,进一步对拦截概率作渐近分析,获得了各中继选择方案的分集阶数。具体地,方案一的分集阶数为1,另外2个中继选择方案的分集阶数均为中继节点个数M。数值结果验证了理论分析得到的结论。     

论科学证据的两种概率解释

在科学证据研究中,存在＂正相关＂和＂高概率＂两种科学证据的概率定义。从这两个定义出发,能够比较清晰直观地解释科学证据的许多相关问题,如科学证据中的＂比较级＂和＂可接受性＂等。然而,两个定义自身存在着不可忽视的缺陷：＂正相关＂不是证据的充要条件,＂高概率＂也不足以使证据之为证据。