Liu-type estimator in semiparametric partially linear additive models

Partially linear additive model is useful in statistical modelling as a multivariate nonparametric fitting technique. This paper considers statistical inference for the semiparametric model in the presence of multicollinearity. Based on the profile least-squares (PL) approach and Liu estimation method, we propose a PL Liu estimator for the parametric component. When some additional linear restrictions on the parametric component are available, the corresponding restricted Liu estimator for the parametric component is constructed. The properties of the proposed estimators are derived. Some simulations are conducted to assess the performance of the proposed procedures and the results are satisfactory. Finally, a real data example is analysed.     

Nonparametric likelihood and doubly robust estimating equations for marginal and nested structural models

This article considers Robins's marginal and nested structural models in the cross‐sectional setting and develops likelihood and regression estimators. First, a nonparametric likelihood method is proposed by retaining a finite subset of all inherent and modelling constraints on the joint distributions of potential outcomes and covariates under a correctly specified propensity score model. A profile likelihood is derived by maximizing the nonparametric likelihood over these joint distributions subject to the retained constraints. The maximum likelihood estimator is intrinsically efficient based on the retained constraints and weakly locally efficient. Second, two regression estimators, named hat and tilde, are derived as first‐order approximations to the likelihood estimator under the propensity score model. The tilde regression estimator is intrinsically and weakly locally efficient and doubly robust. The methods are illustrated by data analysis for an observational study on right heart catheterization. The Canadian Journal of Statistics 38: 609–632; 2010 © 2010 Statistical Society of Canada     

Model‐based linear clustering

The authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set. For each linear cluster, an errors‐in‐variables model is assumed. The optimization of the derived profile likelihood can be achieved by an EM algorithm. Its asymptotic properties and its relationships with several existing clustering methods are discussed. Methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purposes. The Canadian Journal of Statistics 38: 716–737; 2010 © 2010 Statistical Society of Canada     

Statistical inference for multivariate partially linear regression models

In this paper we study a class of multivariate partially linear regression models. Various estimators for the parametric component and the nonparametric component are constructed and their asymptotic normality established. In particular, we propose an estimator of the contemporaneous correlation among the multiple responses and develop a test for detecting the existence of such contemporaneous correlation without using any nonparametric estimation. The performance of the proposed estimators and test is evaluated through some simulation studies and an analysis of a real data set is used to illustrate the developed methodology. The Canadian Journal of Statistics 41: 1–22; 2013 © 2013 Statistical Society of Canada     

A semiparametric nonlinear mixed model approach to phase I profile monitoring

When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric (P) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric (NP) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring (NMRPM) method, it proposes a semiparametric (SP) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods (P, NP, and NMRPM) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling’s T² statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.     

Economic-statistical design of simple linear profiles with variable sampling interval

Control charts are statistical tools to monitor a process or a product. However, some processes cannot be controlled by monitoring a characteristic; instead, they need to be monitored using profiles. Economic-statistical design of profile monitoring means determining the parameters of a profile monitoring scheme such that total costs are minimized while statistical measures maintain proper values. While varying sampling interval usually increases the effectiveness of profile monitoring, economic-statistical design of variable sampling interval (VSI) profile monitoring is investigated in this paper. An extended Lorenzen–Vance function is used for modeling total costs in VSI model where the average time to signal is employed for depicting the statistical measure of the obtained profile monitoring scheme. Two sampling intervals; number of set points and the parameters of control charts that are used in profile monitoring are the variables that are obtained thorough the economic-statistical model. A genetic algorithm is employed to optimize the model and an experimental design approach is used for tuning its parameters. Sensitivity analysis and numerical results indicate satisfactory performance for the proposed model.     

中国学术史研究的主要体式与成果

中国学术史源远流长,其研究方法和形式也丰富多样,一有以庄周《庄子·天下篇》为发端的序跋体,二有以司马迁《史记·儒林列传》为发端的传记体,三有以刘向《七略》为发端的目录体,四有以程颐《河南程氏遗书》为发端的笔记体,五有以朱熹《伊洛渊源录》为发端的道录体,六有以黄宗羲《宋元学案》为发端的学案体,七有以梁启超《中国近三百年学术史》为代表的章节体,八有以民国期间刘汝霖《汉晋学术编年》、《东晋南北朝学术编年》为代表的学术编年体。文章对这八种学术史研究形式的起源、发展、特点、价值等作了全面系统的梳理,揭示了中国学术史研究的主要方式和基本成就。     

Patterns of conflict and enrichment in work-family balance: A three-dimensional typology

Abstract

A four-dimensional typology of work-family balance (WFB) that has previously been reported was tested in four samples: Finnish nurses (n=662), Slovenian nurses (n=667), Finnish health and social care workers (n=1493) and Finnish service sector employees (n=827). Latent profile analyses identified three rather than four types of WFB in each sample. In line with the four-dimensional typology, a Beneficial type (56% to 70% of the participants), experiencing high work-to-family and family-to-work enrichment (WFE and FWE) and low work-to-family and family-to-work conflict (WFC and FWC), and an Active type (15% to 20%), experiencing high WFC, FWC, WFE and FWE, emerged; however, the Harmful and Passive types were not identified. Instead, a Contradictory type (16% to 53%), experiencing high WFC and low WFE in combination with low FWC and high FWE, was found. The Beneficial type showed higher subjective well-being than the Contradictory and Active types, the latter showing higher vigour at work but also higher psychological strain and lower life satisfaction than the Contradictory type. These findings support the view that WFB is not a single state or continuum, but manifests itself in diverse combinations of WFC, FWC, WFE and FWE.     

Emotional labour profiles: Associations with key predictors and outcomes

ABSTRACT

The present study examines how three emotional labour strategies (hiding feelings, faking emotions, and deep acting) combine within different profiles of workers among two samples characterised by different types and intensity of customer contact. In addition, this research investigates the role of perceived workload as well as perceived organisational support, supervisor support, and colleagues support in the prediction of profile membership. Finally, this research also documents the relation between emotional labour profiles and adaptive and maladaptive work outcomes (job satisfaction, work performance, emotional exhaustion, sleeping problems, psychological detachment, and counterproductive work behaviours). Latent profile analysis revealed three similar emotional labour profiles in both samples. Results also showed the most desirable levels on all outcomes to be associated with Profile 3 (Low Emotional Labor/Low Surface Acting and Moderate Deep Acting), followed by Profile 2 (Moderate Emotional Labor/Moderate Surface Acting and High Deep Acting) and Profile 1 (High Emotional Labor), with most comparisons being statistically significant in both samples. In contrast, a more diversified pattern of findings was observed in the prediction of profile membership. For instance, perceived colleagues support did not predict membership into any of the profiles, while supervisor support predicted an increased likelihood of membership into Profile 3 relative to Profiles 1 and 2.