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In this article, we investigate a new estimation approach for the partially linear single-index model based on modal regression method, where the non parametric function is estimated by penalized spline method. Moreover, we develop an expection maximum (EM)-type algorithm and establish the large sample properties of the proposed estimation method. A distinguishing characteristic of the newly proposed estimation is robust against outliers through introducing an additional tuning parameter which can be automatically selected using the observed data. Simulation studies and real data example are used to evaluate the finite-sample performance, and the results show that the newly proposed method works very well. [ABSTRACT FROM AUTHOR]

In public–private partnership (PPP) projects, during the operational period, private investors are prone to act opportunistically in pursuit of their own gain. Based on the perspectives of government’s administrative supervisory functions, this paper analyses the issue of strategic choice for opportunistic behaviour by the government and private investors during the operational period of projects using evolutionary game theory. The results show that there is not a single set of evolutionarily stable strategies (ESS) between the government and the investors. The end results of the evolutionary game are related to the initial states of the system. Conditions that would dissuade investors from adopting an opportunistic behaviour, and settings that would encourage government to supervise closely, are explored. Punishments set by the government, which would then lead to different ultimate choices made by these two parties, are also discussed. Finally, the paper proposes a few policy recommendations for government supervision on the basis of parametric analysis. The findings also serve as a reference for the decision-making process of the government and the investors. [ABSTRACT FROM PUBLISHER]

In this paper, the semi varying coefficient zero-inflated generalized Poisson model is discussed based on penalized log-likelihood. All the coefficient functions are fitted by penalized spline (P-spline), and Expectation-maximization algorithm is used to drive these estimators. The estimation approach is rapid and computationally stable. Under some mild conditions, the consistency and the asymptotic normality of these resulting estimators are given. The score test statistics about dispersion parameter is discussed based on the P-spline estimation. Both simulated and real data example are used to illustrate our proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the generalized varying-coefficient single-index model is discussed based on penalized likelihood. All the unknown functions are fitted by penalized spline. The estimates of the unknown parameters and the unknown coefficient functions are obtained and the estimation approach is rapid and computationally stable. Under some mild conditions, the consistency and the asymptotic normality of these resulting estimators are given. Two simulation studies are carried out to illustrate the performance of the estimates. An application of the model to the Hong Kong environmental data further demonstrates the potential of the proposed modelling procedures. [ABSTRACT FROM PUBLISHER]

The beta regression models are commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). In this paper, we consider the issue of variable selection for beta regression models with varying dispersion (VBRM), in which both the mean and the dispersion depend upon predictor variables. Based on a penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. Following the coordinate descent algorithm idea of generalized linear models, we develop new variable selection procedure for the VBRM, which can efficiently simultaneously estimate and select important variables in both mean model and dispersion model. Simulation studies and body fat data analysis are presented to illustrate the proposed methods. [ABSTRACT FROM PUBLISHER]