Flexible Servers in Understaffed Tandem Lines

We study the dynamic assignment of cross‐trained servers to stations in understaffed lines with finite buffers. Our objective is to maximize the production rate. We identify optimal server assignment policies for systems with three stations, two servers, different flexibility structures, and either deterministic service times and arbitrary buffers or exponential service times and small buffers. We use these policies to develop server assignment heuristics for Markovian systems with larger buffer sizes that appear to yield near‐optimal throughput. In the deterministic setting, we prove that the best possible production rate with full server flexibility and infinite buffers can be attained with partial flexibility and zero buffers, and we identify the critical skills required to achieve this goal. We then present numerical results showing that these critical skills, employed with an effective server assignment policy, also yield near‐optimal throughput in the Markovian setting, even for small buffer sizes. Thus, our results suggest that partial flexibility is sufficient for near‐optimal performance, and that flexibility structures that are effective for deterministic and infinite‐buffered systems are also likely to perform well for finite‐buffered stochastic systems.     

Expert rule versus majority rule under partial information

We study the uncertain dichotomous choice model. Under some assumptions on the distribution of expertise of the various panel members, the probability of the expert rule to be the optimal one is compared to that of the majority rule to be optimal. It turns out that for the former probability exceeds the latter by far, especially as the panel size becomes large.     

On Nelson–Aalen type estimation in the partial Koziol–Green model

In this paper, a Nelson–Aalen (NA) type estimator is derived and its sample properties are compared with the partial Abdushukurov–Cheng–Lin (PACL), generalized maximum likelihood (GMLE), and Kaplan–Meier (KM) estimators under the partial Koziol–Green model. These comparisons are made through Monto Carlo simulations under various sample sizes. The results indicate that the NA estimator always performs better than the KM estimator and is competitive with other estimators. Moreover, the PACL, GMLE, and NA estimators are shown to be asymptotically equivalent.     

Minimum average partial correlation and parallel analysis: The influence of oblique structures

ABSTRACT

Parallel analysis (Horn 1965) and the minimum average partial correlation (MAP; Velicer 1976) have been widely spread as optimal solutions to identify the correct number of axes in principal component analysis. Previous results showed, however, that they become inefficient when variables belonging to different components strongly correlate. Simulations are used to assess their power to detect the dimensionality of data sets with oblique structures. Overall, MAP had the best performances as it was more powerful and accurate than PA when the component structure was modestly oblique. However, both stopping rules performed poorly in the presence of highly oblique factors.     

Comparison of prediction methods for multicollinear data

In this paper we discuss the partial least squares (PLS) prediction method. The method is compared to the predictor based on principal component regression (PCR). Both theoretical considerations and computations on artificial and real data are presented.     

Testing for changes in autocovariances of nonparametric time series models

In the literature on change-point analysis, much attention has been paid to detecting changes in certain marginal characteristics, such as mean, variance, and marginal distribution. For time series data with nonparametric time trend, we study the change-point problem for the autocovariance structure of the unobservable error process. To derive the asymptotic distribution of the cumulative sum test statistic, we develop substantial theory for uniform convergence of weighted partial sums and weighted quadratic forms. Our asymptotic results improve upon existing works in several important aspects. The performance of the test statistic is examined through simulations and an application to interest rates data.     

Lasso penalized semiparametric regression on high-dimensional recurrent event data via coordinate descent

This paper studies a fast computational algorithm for variable selection on high-dimensional recurrent event data. Based on the lasso penalized partial likelihood function for the response process of recurrent event data, a coordinate descent algorithm is used to accelerate the estimation of regression coefficients. This algorithm is capable of selecting important predictors for underdetermined problems where the number of predictors far exceeds the number of cases. The selection strength is controlled by a tuning constant that is determined by a generalized cross-validation method. Our numerical experiments on simulated and real data demonstrate the good performance of penalized regression in model building for recurrent event data in high-dimensional settings.     

Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models

The aim of this paper is to define a new approach, called Hybrid Two-Step, to estimate the parameters of a second-order latent variable (LV) model in the case of formative relationships between the first-order and the second-order LVs. In this respect, we introduce the two main approaches to the estimation of second-order constructs through the partial least squares-path modelling: the so-called Repeated Indicators approach and the Two-Step approach. Some criticisms of these methodologies are highlighted and a solution to the issue of the identification of formative second-order constructs is suggested through the adoption of a Hybrid Two-Step approach. A Monte Carlo simulation study aimed at comparing the approach proposed with the traditional ones was performed. Finally, a case study about the passenger satisfaction is presented to show the implementation of the method and to give some comparative empirical results.     

我国R&D强度的影响因素——基于局部调整模型的实证检验

采用1991～2007年时间序列数据实证研究了影响我国R&D强度的因素。首先从理论上分析了影响R&D强度的5个因素:企业因素、政策因素、公共R&D部门、投资因素、经济因素;根据数据的可获得性设计了企业R&D经费投入强度、企业R&D人员投入强度、行业结构、政府直接R&D补贴、知识产权保护、公共研发部门R&D支出的力度、固定资产投资、人力资本投资、人均GDP、GDP增长率、外贸依存度、外商直接投资12个变量。以局部调整模型为基础设计了实证模型,通过分析实证结果,发现企业R&D经费投入强度、行业结构、知识产权保护和固定资产投资等因素对R&D强度具有显著的正向影响。     

The Nonparametric Identification of Treatment Effects in Duration Models

This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define “no anticipation of treatment” and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single‐spell and multiple‐spell data. The timing of events conveys useful information on the treatment effect.