Search

We propose a novel partitioning clustering procedure based on the cumulative distribution function (CDF), called K-CDFs. For univariate data, the K-CDFs represent the cluster centers by empirical CDFs and assign each observation to the closest center measured by the Cram e ´ r-von Mises distance. The procedure is nonparametric and does not require assumptions on cluster distributions imposed by mixture models. A projection technique is used to generalize the K-CDFs for univariate data to an arbitrary dimension. The proposed procedure has several appealing properties. It is robust to heavy-tailed data, is not sensitive to the data dimensions, does not require moment conditions on data and can effectively detect linearly nonseparable clusters. To implement the K-CDFs, we propose two kinds of algorithms: a greedy algorithm as the classical Lloyd's algorithm and a spectral relaxation algorithm. We illustrate the finite sample performance of the proposed algorithms through simulation experiments and empirical analyses of several real datasets. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

• Demand motivation and environmental factors drive or restrain excessive behavior. • Increasing the sense of responsibility to reduce the government's excessive behavior. • Demand has intermediary role in impact of external environment on excessive behavior. • Attaching the direct negative impact of lack of project information on behavior. • Strengthening government supervision on others besides the government-paid project. The excessive behavior of the government is a common phenomenon in the practice of PPP projects such as transportation, municipal engineering and environmental governance. Analyzing the excessive behaviors of the government in PPP projects is very important to improve project performance and success. Based on the field dynamic theory, the demand of government and the external environment participating in PPP are the important reasons for excessive behavior. Through literature analysis, 14 government demands and 10 environmental factors that influence the government's excessive behavior are identified, and a hypothesis model of the influencing path of government's excessive behavior is constructed. 166 valid data are collected through a questionnaire survey. Carrying out factor analysis, hypothesis, and path test, the main findings are as follows: Government's demands are divided into the demand of satisfying public activities, self-interest, responsibility, and relieving financial pressure; External environmental factors are divided into social conditions, department mechanism, and project information; The demand for responsibility plays a restraining role, while the other types of demand are opposite, and these demands play an intermediary role in the impact path of external environment on excessive behavior; Among the environmental factors, only the lack of project information has a direct influence on the government excessive behavior; In the feasibility subsidy project, the risk of excessive behavior is high, especially for transportation projects with certain toll nature. The study provides suggestions for reducing the risk of government behavior in PPP projects. [ABSTRACT FROM AUTHOR]

The difference in restricted mean survival times between two groups is often of inherent interest in epidemiologic and medical studies. In this paper, we propose a general additive-multiplicative hazards (AMHs) regression model to estimate the restricted mean treatment effects, where the survival time is subject to both dependent and independent censoring. The AMH specifies an additive and multiplicative form on the hazard functions for the survival and censored times associated with covariates, which contains the proportional hazards model and the additive hazards model. By an inverse probability censoring weights scheme, we obtain the estimators of the regression parameters and the restricted mean treatment effects. We establish the large sample properties of the proposed estimators. Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedures and the primary biliary cirrhosis patients data are analyzed for illustration. [ABSTRACT FROM AUTHOR]

In public–private partnership (PPP) projects, state-owned investors, with their administrative attributes, tend to be more concerned with their own face-saving (self-face) concerns, whereas private investors that partner with the government pay more attention to the government's face-saving (other-face) concerns than their own. The two kinds of face-saving concerns both aggravate escalation of commitment (EOC). Accordingly, a multigroup model based on questionnaire data was built to analyze the differences in face-saving concerns of the two types of investors and the cross-enterprise differences that influence investors' EOC. The main findings are as follows: state-owned investors prefer self-face concerns; the influence of face-saving concerns on investors' EOC does not differ across enterprises; and self-face concerns and other-face concerns each have significant influence on both kinds of investors' EOC, but the latter plays a larger role. The study enriches the understanding of EOC and emphasizes the advantages of private capital in reducing investors' behavioral risk. The findings are conducive to improving the mechanism and control of multistakeholder EOC in PPP projects. [ABSTRACT FROM AUTHOR]

In public-private partnership (PPP) projects, as the largest funds provider, financial institutions only supervise the cash flow in the project's implementation stage. Since financial institutions are not involved in the project's preliminary planning stage, they have inadequate risk management to projects, leading to adverse effects on the following project's implementation. This paper proposes that financial institutions intervene in PPP projects early, use their resources to make reasonable plans for the project, enhance the identification and control of project risks, and improve the project profitability and successful implementation possibility. Based on the transaction cost theory, this paper constructs the incentive mechanism and value-added Stackelberg game model of PPP projects under the scenario that financial institutions intervene early. Considering financial institutions intervene in PPP early, the government will reduce planning costs and private investors will save financing costs. The results show that financial institutions' early intervention in PPP projects can enhance the government's efforts, private investors' efforts, the government's incentive intensity, and also the project's value-added level. Compared with financial institutions that do not intervene in advance, the entire model can achieve Pareto improvement. This paper is conducive to expanding the application of transaction cost theory in PPP projects. In addition, it can also be conducive to improving the status quo that financial institutions cannot participate in PPP projects in the early stage and promoting the successful implementation of PPP projects. [ABSTRACT FROM AUTHOR]

The early termination of public–private partnerships (PPP) frequently occurs in large infrastructure projects. Studies have analyzed the issue of compensation arising from early termination; however, most have ignored optionality in managing the risks and uncertainties embedded in these settings. Using real option theory, this paper examines pricing mechanisms for early termination under two different scenarios, namely, those of excessively high or excessively low cash flows, wherein early termination of PPP projects occurs during the stage of the operation prior to the final stage of asset transfer. The results show that while the pricing of early termination is mainly determined by the actual cash flow profile, it is also related to the level of total investment and return on investment. The final value of compensation agreed upon is also dependent on the bargaining power of each party. These findings contribute new insights into the pricing mechanisms of early termination used by governments and private investors. The quantification and derivation of early termination prices up front serves as a valuable benchmark and a good starting point for both governments and investors during negotiations and addresses the issue of compensation for early termination in a fair and reasonable manner. [ABSTRACT FROM AUTHOR]

This paper is concerned with how to select significant variables in the partially linear varying-coefficient hazard model for multivariate survival data. A new variable selection procedure is proposed to simultaneously estimate the parameters and select variables for the parametric parts. Compared to the profile pseudo-partial likelihood proposed by Cai et al. (2008), the advantage of our method is to be practically feasible and easily implemented. We show that the estimators of both the parametric and nonparametric parts achieve the best convergence rates and establish their asymptotic normality. Moreover, we demonstrate that proposed procedures perform as well as an oracle procedure. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed procedures and a real dataset from the Colon Cancer Study is analyzed for illustration. [ABSTRACT FROM AUTHOR]

Abstract: Single index models are natural extensions of linear models and overcome the so-called curse of dimensionality. They have applications to many fields, such as medicine, economics and finance. However, most existing methods based on least squares or likelihood are sensitive when there are outliers or the error distribution is heavy tailed. Although an M-type regression is often considered as a good alternative to those methods, it may lose efficiency for normal errors. In this paper, we propose a new robust and efficient estimation procedure based on local modal regression for single index models. The asymptotic normality of proposed estimators for both the parametric and nonparametric parts is established. We show that the proposed estimators are as asymptotically efficient as the least-square-based estimators when there are no outliers and the error distribution is normal. A modified EM algorithm is presented for efficient implementation. The simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed method. [Copyright &y& Elsevier]

In public–private partnership (PPP) project, government's control right (GCR) cannot be completely defined by contract information. Government has some advantageous information resources outside of the contract, such as status, supervision level, trust etc. These information lead investors to overestimate the GCR and aggravate the escalation of commitment (EOC), especially for investors who prefer the other-face concern. According to the degree of quantification of information owned by government in a PPP project, the study divided GCR into explicit control rights (ECR) and implicit control rights (ICR). In order to discuss the difference of actions that two types' control right on investor's EOC who have other-face concern. The study collected 176 valid data through scenario questionnaires and constructed the structural equation model with regulating effect. After verifying the model with AMOS. We found the ECR influence investor's EOC significantly, while the ICR does not; the other-face concern only regulate the influence of ICR on investors' EOC. These findings are helpful to reduce the behavioral risk of investors' EOC in PPP project. • Escalation of commitment (EOC) is discussed in PPP project, which offers new insight. • 14 secondary indicators can measure government's control rights (GCR) in PPP project. • GCR includes explicit control rights (ECR) and implicit control rights (ICR). • ECR influence investor's EOC significantly, while the ICR does not. • Other-face concern regulates the influence of ICR on investors' EOC. [ABSTRACT FROM AUTHOR]

Long project cycle and uncertainties are important characteristics of public-private partnership (PPP) projects. Since the introduction of PPP projects in China, the timing of capital withdrawal has become important. With the emergence of risk factors during the course of the project, it will face the problem of investment withdrawal by social capital financial investors. Escalation of commitment (EOC) refers to the erroneous behaviour of project decision makers who do not promptly withdraw from a project when they receive negative feedback and continue to invest resources in the project. EOC not only causes more unnecessary losses but also adversely affects decision makers. Therefore, it is crucial to clarify the impact of EOC on the choice of the exit timing of social capital. This article adopts literature survey method and quantitative analysis method: introducing the theory of maximization of income into the real option model, combining the net present value method with the binary tree option pricing model, constructing the decision-making model to analyze the exit timing of PPP social capital in the context of EOC. Then combined numerical simulation and empirical analysis to verify the effectiveness of the decision-making model, discussed the reasons why the social capital party chooses EOC, and proposes measures for controlling EOC. The higher the degree of completion of the project, the easier it is for the person in charge of the project to make inaccurate judgements about the project due to personal psychological factors, and the easier it is for EOC to occur. Therefore, after setting the minimum goal of the project, the decision maker needs to accurately evaluate the existing value of the project to avoid falling into decision-making errors. [ABSTRACT FROM AUTHOR]

• Investor may try to keep returns below guarantee level for government's compensation. • To prevent investors' speculations, government should set incentive mechanisms. • A reasonable incentive mechanism can affect investors' attitude greatly. Mechanisms for risk sharing are critical in infrastructure project financing. In public-private partnerships (PPP) for highway projects, provision of guarantees by the government to the investors is an important risk-sharing scheme. Because the government does not have the same information that private investors have, information asymmetry leads to speculation. In this paper, investors grab minimum revenue guarantee by deliberately reducing in earnings is identified as a moral hazard. To solve this problem, a game theory model for studying mechanisms that encourage/incentivize investors to adopt a positive attitude toward cooperation with the government is established. In particular, we design incentive mechanisms that engage private investors based on their profitability outlook. The results show that blindly increasing the amount of a reward or a penalty is not an effective way to guide an investor's behavior. Adopting flexible incentive mechanisms during different stages of a project proves effective in influencing investors' willingness to cooperate with the government as well as in maximizing the benefits they realize from the project. [ABSTRACT FROM AUTHOR]

Ultra-high-dimensional data are frequently seen in many contemporary statistical studies, which pose challenges both theoretically and methodologically. To address this issue under longitudinal data setting, we propose a marginal nonparametric screening method to hunt for the relevant covariates in additive models. A new data-driven thresholding and an iterative procedure are developed. Especially, a sample splitting method is proposed to further reduce the false selection rates. Although the repeated measurements within each subjects are correlated, the sure screening property is theoretically established. To the best of our knowledge, screening for longitudinal data rarely appeared in the literatures, and our method can be regarded as a nontrivial extension of nonparametric independence screening method. An extensive simulation study is conducted to illustrate the finite sample performance of the proposed method and procedure. Finally, the proposed method is applied to a yeast cycle gene expression data set to identify cell cycle-regulated genes and transcription factors. [ABSTRACT FROM AUTHOR]

This work explored the antibacterial mechanism of linalool on Shigella sonnei (S. sonnei) based on membrane damage driven by oxidative stress. The results revealed that linalool had excellent inhibition towards S. sonnei under a concentration of 1.5 mL/L. Furthermore, cell structure was disrupted with massive leakage of K+ and Mg2+ with linalool according to scanning electron microscopy, zeta potential and probes staining of fluorescein diacetate and Rhodamine 123 combined with fluorescence spectroscopy. Changes in membrane protein conformation were also monitored as a result of fluorescence spectroscopy when exposed to linalool. In addition, oxidative stress was appeared after exposure to linalool due to the increase of malondialdehyde, hydrogen peroxide, superoxide anion and oxidized glutathione, and the decrease of reduced glutathione. The activity of defense enzyme including superoxide dismutase, catalase and peroxidase, and antioxidant enzyme including glutathione reductase and glutathione S-transferase was also inhibited. In all, oxidative stress-driven membrane damage was considered as the main mechanism by which linalool inhibited S. sonnei. Our findings provide theoretical support for linalool as a promising natural antibacterial agent against S. sonnei contamination. [Display omitted] • Membrane damage of Shigella sonnei was detected in the presence of linalool. • The conformation of membrane proteins in Shigella sonnei was altered with linalool. • ROS accumulation and lipid peroxidation occurred in the presence of linalool. • The free radical scavenging ability in cells was weakened after adding linalool. [ABSTRACT FROM AUTHOR]

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 group Lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level [27]. Quantile group Lasso, a natural extension of quantile Lasso [25], is a good alternative when the data has group information and has many outliers and/or heavy tails. How to discover important features that are correlated with interest of outcomes and immune to outliers has been paid much attention. In many applications, however, we may also want to keep the flexibility of selecting variables within a group. In this paper, we develop a sparse group variable selection based on quantile methods which select important covariates at both the group level and within the group level, which penalizes the empirical check loss function by the sum of square root group-wiseL1-norm penalties. The oracle properties are established where the number of parameters diverges. We also apply our new method to varying coefficient model with categorial effect modifiers. Simulations and real data example show that the newly proposed method has robust and superior performance. [ABSTRACT FROM PUBLISHER]

Semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modeling. With high-dimensional covariates both in parametric and nonparametric part for SPLVCM, sparse modeling is often considered in practice. In this paper, we propose a new estimation and variable selection procedure based on modal regression, where the nonparametric functions are approximated by $$B$$ -spline basis. The outstanding merit of the proposed variable selection procedure is that it can achieve both robustness and efficiency by introducing an additional tuning parameter (i.e., bandwidth $$h$$ ). Its oracle property is also established for both the parametric and nonparametric part. Moreover, we give the data-driven bandwidth selection method and propose an EM-type algorithm for the proposed method. Monte Carlo simulation study and real data example are conducted to examine the finite sample performance of the proposed method. Both the simulation results and real data analysis confirm that the newly proposed method works very well. [ABSTRACT FROM AUTHOR]

The varying coefficient model (VCM) is an important generalization of the linear regression model and many existing estimation procedures for VCM were built onL2loss, which is popular for its mathematical beauty but is not robust to non-normal errors and outliers. In this paper, we address the problem of both robustness and efficiency of estimation and variable selection procedure based on the convex combined loss ofL1andL2instead of only quadratic loss for VCM. By using local linear modeling method, the asymptotic normality of estimation is driven and a useful selection method is proposed for the weight of compositeL1andL2. Then the variable selection procedure is given by combining local kernel smoothing with adaptive group LASSO. With appropriate selection of tuning parameters by Bayesian information criterion (BIC) the theoretical properties of the new procedure, including consistency in variable selection and the oracle property in estimation, are established. The finite sample performance of the new method is investigated through simulation studies and the analysis of body fat data. Numerical studies show that the new method is better than or at least as well as the least square-based method in terms of both robustness and efficiency for variable selection. [ABSTRACT FROM PUBLISHER]

The semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modelling, but most existing methods were built on either the least-square or likelihood-based methods, which are very sensitive to the outliers and their efficiency may be significantly reduced for heavy tail error distribution. This paper proposes a new efficient and robust estimation procedure for the SPLVCM based on modal regression. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts, and show that the estimators achieve the best convergence rate. Moreover, we develop a variable selection procedure to select significant parametric components for the SPLVCM and prove the method possessing the oracle property. We also discuss the bandwidth selection and propose an expectation–maximisation-type algorithm for the proposed estimation procedure. Some simulation results and real data analysis confirm that the newly proposed method works very competitively compared to other existing methods. [ABSTRACT FROM AUTHOR]

In this paper, a minimizing average check loss estimation (MACLE) procedure is proposed for the single-index coefficient model (SICM) in the framework of quantile regression (QR). The resulting estimators have the asymptotic normality and achieve the best convergence rate. Furthermore, a variable selection method is investigated for the QRSICM by combining MACLE method with the adaptive LASSO penalty, and we also established the oracle property of the proposed variable selection method. Extensive simulations are conducted to assess the finite sample performance of the proposed estimation and variable selection procedure under various error settings. Finally, we present a real-data application of the proposed approach. [ABSTRACT FROM AUTHOR]

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]

Public-Private-Partnership (PPP) refinancing is a complex activity with significant uncertainty, including environment-related and decision-maker-related uncertainty. Although value evaluation techniques, like Real Option (RO) and Fuzzy Real Option, have been developed to deal with uncertainty, the reliability of the used project information is ignored. This study aims to establish a refinancing value evaluation model considering both uncertainty and reliability. A Z-number based Real Option (ZRO) model is proposed using Z-numbers to represent the parameter values in the traditional RO model. In the Z-numbers, subjective evaluation of project parameters and information reliability are characterized. Moreover, a novel method is proposed to objectively evaluate the expert's reliability by considering the expert's multiple characteristics. The proposed model can determine refinancing values under various scenarios and reliability. A numerical case is used to validate the model performance. The results show that the refinancing value becomes higher when the information reliability increases. Changes in the underlying project value affect the refinancing option value most, while the impact of the annual project volatility is minimal. The proposed ZRO model could be applied to search for potential investors and as a bargaining tool. [ABSTRACT FROM AUTHOR]

In this paper, we propose a composite minimizing average check loss estimation procedure for composite quantile regression (CQR) in the single-index coefficient model (SICM). The asymptotic normalities of the proposed estimators are established, and the asymptotic relative efficiencies (ARE) of the proposed estimators compared with those by least square method are also discussed. We further investigate a variable selection procedure by combining the proposed estimation method with adaptive LASSO penalized method in CQR of SICM. The oracle property of the proposed variable selection method is also established. Simulations with various non-normal errors and one real data application are conducted to assess the finite sample performance of the proposed estimation and variable selection methods. [ABSTRACT FROM AUTHOR]

Single index models are natural extensions of linear models and overcome the so-called curse of dimensionality. They are very useful for longitudinal data analysis. In this paper, we develop a new efficient estimation procedure for single index models with longitudinal data, based on Cholesky decomposition and local linear smoothing method. Asymptotic normality for the proposed estimators of both the parametric and nonparametric parts will be established. Monte Carlo simulation studies show excellent finite sample performance. Furthermore, we illustrate our methods with a real data example. [ABSTRACT FROM PUBLISHER]

Partial linear single-index model (PLSIM) is a flexible and applicable model when investigating the underlying relationship between the response and the multivariate covariates. Most previous studies on PLSIM concentrated on mean regression, based on least square or likelihood approach. In contrast to this method, in this paper, we propose minimizing average check loss estimation (MACLE) procedure to conduct quantile regression of PLSIM. We construct an initial consistent quantile regression estimator of the parametric part base multi-dimensional kernels, and further promote the estimation efficiency to the optimal rate. We discuss the optimal bandwidth selection method and establish the asymptotic normality of the proposed MACLE estimators. Furthermore, we consider an adaptive lasso penalized variable selection method and establish its oracle property. Simulation studies with various distributed error and a real data analysis are conducted to show the promise of our proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties of the proposed estimator are derived. Furthermore, we propose a variable selection procedure for the QR of SIM by combining the estimation method with the adaptive LASSO penalized method to get sparse estimation of the index parameter. The oracle properties of the variable selection method are established. Simulations with various non-normal errors are conducted to demonstrate the finite sample performance of the estimation method and the variable selection procedure. Furthermore, we illustrate the proposed method by analyzing a real data set. [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]

Discusses the measurement of isotopes using the cathodoluminescence imaging and ion microprobe zircon dating, at the uppermost Cretaceous Furao Formation near the Heilong River in China. Features of zircons; Outcrop of the Furaoan Stage in Baitoushan Section; Estimate of the formation age.