Semiparametric analysis of longitudinal data with informative observation times and censoring times

We focus on regression analysis of irregularly observed longitudinal data which often occur in medical follow-up studies and observational investigations. The model for such data involves two processes: a longitudinal response process of interest and an observation process controlling observation times. Restrictive models and questionable assumptions, such as Poisson assumption and independent censoring time assumption, were posed in previous works for analysing longitudinal data. In this paper, we propose a more general model together with a robust estimation approach for longitudinal data with informative observation times and censoring times, and the asymptotic normalities of the proposed estimators are established. Both simulation studies and real data application indicate that the proposed method is promising.     

Adaptive resampling algorithms for estimating bootstrap distributions

Based on recent developments in the field of operations research, we propose two adaptive resampling algorithms for estimating bootstrap distributions. One algorithm applies the principle of the recently proposed cross-entropy (CE) method for rare event simulation, and does not require calculation of the resampling probability weights via numerical optimization methods (e.g., Newton's method), whereas the other algorithm can be viewed as a multi-stage extension of the classical two-step variance minimization approach. The two algorithms can be easily used as part of a general algorithm for Monte Carlo calculation of bootstrap confidence intervals and tests, and are especially useful in estimating rare event probabilities. We analyze theoretical properties of both algorithms in an idealized setting and carry out simulation studies to demonstrate their performance. Empirical results on both one-sample and two-sample problems as well as a real survival data set show that the proposed algorithms are not only superior to traditional approaches, but may also provide more than an order of magnitude of computational efficiency gains.     

Specification Test for Spatial Autoregressive Models

This article considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix W_n is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove the consistency of our test. To improve the finite sample performance of our test, we also propose a residual-based wild bootstrap and justify its asymptotic validity. We conduct a small set of Monte Carlo simulations to investigate the finite sample properties of our tests. Finally, we apply the test to two empirical datasets: the vote cast and the economic growth rate. We reject the linear spatial autoregressive model in the vote cast example but fail to reject it in the economic growth rate example. Supplementary materials for this article are available online.     

一种基于函数型数据的综合评价方法研究

 在经济管理与决策中, 经常遇到大量的函数型数据。当指标为函数型数据时,提出了一种基于函数型数据的综合评价方法,而综合评价的核心是评价指标在不同时刻的权重系数的确定。针对由函数型数据表支持的综合评价问题的特殊性,提出了一种新的确定权重系数的“全局”拉开档次法,利用Matlab编程,使得该方法具有可操作性,并给出一个实际例子。最后将该方法与传统方法进行比较,得出本文所提方法的优势。     

Comparisons of normalization methods for relative quantitative in real-time polymerase chain reaction

The real-time polymerase chain reaction (rtPCR) provides sensitive and accurate quantitative results and becomes a widespread technique in analyzing gene expressions. House-keeping genes are required as references to normalize data of target genes, which may be unstable. This normalization process is similar to the normalization in analyzing high-density oligonucleotide arrays. This article evaluates the feasibility of normalizations for high-density oligonucleotide arrays to normalize data collected in rtPCR experiments. Since data features are different, simulations are used to evaluate the performance of these normalizations to rtPCR data based on five indices. Their feasibilities are illustrated by a rtPCR data.     

A tardiness-augmented approximation scheme for rejection-allowed multiprocessor rescheduling

In the multiprocessor scheduling problem to minimize the total job completion time, an optimal schedule can be obtained by the shortest processing time rule and the completion time of each job in the schedule can be used as a guarantee for scheduling revenue. However, in practice, some jobs will not arrive at the beginning of the schedule but are delayed and their delayed arrival times are given to the decision-maker for possible rescheduling. The decision-maker can choose to reject some jobs in order to minimize the total operational cost that includes three cost components: the total rejection cost of the rejected jobs, the total completion time of the accepted jobs, and the penalty on the maximum tardiness for the accepted jobs, for which their completion times in the planned schedule are their virtual due dates. This novel rescheduling problem generalizes several classic NP-hard scheduling problems. We first design a pseudo-polynomial time dynamic programming exact algorithm and then, when the tardiness can be unbounded, we develop it into a fully polynomial time approximation scheme. The dynamic programming exact algorithm has a space complexity too high for truthful implementation; we propose an alternative to integrate the enumeration and the dynamic programming recurrences, followed by a depth-first-search walk in the reschedule space. We implemented the alternative exact algorithm in C and conducted numerical experiments to demonstrate its promising performance.

     

The Rural/Urban Volunteering Divide

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Are rural residents more likely to volunteer than those living in urban places? Although early sociological theory posited...     

供应链视角下的多方并购框架

以并购参与方所在供应链为研究视角,探讨了买方与多个供应商并购分析框架。首先,得出多种框架下并购前后各方所获利润,并数值模拟供应链环境变化对买方利润的影响;其次,在比较基础上给出供应链各方互动下的均衡。结果表明,并购交易的实施和供应链各方互动共同影响并购各方和其余各方所获利润,且互动削弱了并购产生的正向协同效应。本文给出了供应链各方互动下的并购分析框架,进一步完善了并购决策过程中的交易评估内容。