Schwarz Method for Penalized Quasi-Likelihood in Generalized Additive Models

The topic is penalized quasi-maximum likelihood estimation in generalized additive models by an approximation using a sequence of sub-models, here called blocks. The Schwarz method uses a sequence of sub-models, The technique might be useful to model comparison, where the fitted values from a sub-model are used as starting values for a larger model.

We show that the algorithm method converges in canonical link of generalized additive models, and a theorem about bound condition of sub-models convergence with uncanonical link.     

Assessing One-Step-Ahead Prediction Error Based on the Median for First-Order Autoregressive Models in the Presence Of Outliers

The prediction of the one-step-ahead observation of the first-order autoregressive process in the presence of outliers is considered. The mean square of the prediction error is obtained based on the median estimator of the model parameter for a stationary process. Monte Carlo simulation methods are employed to investigate the performance of the proposed estimator as well as the conventional ordinary least squares estimators proposed by Zhang and Shaman (1995 Zhang , P. , Shaman , P. ( 1995 ). Assessing prediction error in autoregressive models . Trans. Amer. Mathemat. Soc. 347 : 627 – 637 .[Crossref], [Web of Science ®] , [Google Scholar]) and Kabaila and He (1999 Kabaila , P. , He , Z. ( 1999 ). On assessing prediction error in autoregressive models . J. Time Ser. Anal. 20 : 663 – 670 .[Crossref] , [Google Scholar]) for a process without outliers. The results show that the proposed method outperforms the conventional method. These conclusions are substantiated with results from actual datasets.     

Empirical Likelihood for Partially Non Linear Models with Missing Response Variables at Random

This article is concerned with partially non linear models when the response variables are missing at random. We examine the empirical likelihood (EL) ratio statistics for unknown parameter in non linear function based on complete-case data, semiparametric regression imputation, and bias-corrected imputation. All the proposed statistics are proven to be asymptotically chi-square distribution under some suitable conditions. Simulation experiments are conducted to compare the finite sample behaviors of the proposed approaches in terms of confidence intervals. It showed that the EL method has advantage compared to the conventional method, and moreover, the imputation technique performs better than the complete-case data.     

论《野草》的多重话语

一向被认为是＂孤独者的心灵独白＂的《野草》其实或明显或隐秘地存在着多重话语。本文的话语主要取其字面意义,即是言语交流单位,一种具有指向性的表述;话语类型主要是按照话语指向的目的来划分的。本文试图通过对《野草》文本的解读,来梳理这些互相交织的多重话语。     

公民资格的解释范式与分析走向

公民资格是政治学研究的重要主题.本文归纳和总结了公民资格的两大解释范式:自由主义范式和共和主义范武.自由主义范式主要表现在洛克、T.H.马歇尔等人的理论建构上,它以个人权利为核心,强调政府对于社会的工具性职能,把公民资格看作是通往个人自由的手段.共和主义范式主要体现在亚里士多德、西塞罗、马基雅维利、卢梭等人的思想中,它把公民美德、公民对公共事务的参与和对共同体的奉献看作是公民资格的核心,把公民资格看作是自由本身.在此基础上,本文还探讨了范武差别和更替后面所反映的深层问题,分析了两大范武在当前社会背景下所面临的挑战及未来走向.     

言语行为论能用来反驳罗素的摹状词理论吗?

约翰·塞尔提出了一种基于言语行为论的论证,支持斯特劳森,反驳罗素的限定摹状词理论。他的论证有两个前提。第一,只有两种可能的方式把罗素的理论应用于所有种类的语内行为:我们要么把每个包含限定摹状词的语内行为都分析为包含有存在断定,或者把它分析为只在命题内容中有存在性表达。第二个前提是两种方式都行不通。我质疑第二个前提并试图表明塞尔并没有给出充分理由证明普通所谓命令或提问不能包含断定。在结尾处,我描述了一种语言现象,即包含限定摹状词的语言可用来进行欺骗,以支持罗素的理论。     

An additive Cox model for coronary heart disease study

Existing models for coronary heart disease study use a set of common risk factors to predict the survival time of the disease, via the standard Cox regression model. For complex relationships between the survival time and risk factors, the linear regression specification in the existing Cox model is not flexible enough to accounts for such relationships. Also, the risk factors are actually risky only when they fall in some risk ranges. For more flexibility in modelling and characterize the risk factors more accurately, we study a semi-parametric additive Cox model, using basis splines and LASSO technique. The proposed model is evaluated by simulation studies and is used for the analysis of a real data in the Strong Heart Study.     

The ruin probabilities of a discrete time risk model with one-sided linear claim sizes and dependent risks

This article investigates the ruin probabilities of a discrete time risk model with dependent claim sizes and dependent relation between insurance risks and financial risks. The risk-free and risky investments of an insurer lead to stochastic discount factors {θ_n}_{n ? 1}. The claim sizes are assumed to follow a one-sided linear process with independent and identically distributed (i.i.d.) innovations {?_n}_{n ? 1}. The i.i.d. random pairs {(?_n, θ_n)}_{n ? 1} follow a common bivariate Sarmanov-dependent distribution. When the common distribution of the innovations is heavy tailed, we establish some asymptotic estimates for the ruin probabilities of this discrete time risk model.     

CBPS-based estimation for linear models with responses missing at random

In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are missing at random. It is proved that the proposed estimators are asymptotically normal. In simulation studies and real example, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.     

Basket CDS pricing with default intensities using a regime-switching shot-noise model

In this paper, we employ an intensity-based credit risk model with regime-switching to study the valuation of basket CDS in a homogeneous portfolio. We assume that the default intensities are described by some dependent regime-switching shot-noise processes and the individual jumps of the intensity are driven by a common factor. By using the conditional Laplace transform of the regime-switching shot-noise process, we obtain the closed form results for pricing the fair spreads of the basket CDS. We present some numerical examples to illustrate the effect of the model parameters on the fair spreads.