SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

GENERALIZED EXTREME VALUE ADDITIVE MODEL ANALYSIS VIA MEAN FIELD VARIATIONAL BAYES

We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Markov Chain Monte Carlo methods. We illustrate our new methodology with maximum rainfall data from the Sydney, Australia, hinterland. Comparisons are made between the Mean Field Variational Bayes and Markov Chain Monte Carlo approaches.     

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.     

NONPARAMETRIC AUTOCOVARIANCE FUNCTION ESTIMATION

Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based on straightforward nonparametric mean function estimation ideas and allow use of any linear smoother (e.g. smoothing spline, local polynomial). The paper studies the properties of the estimators, and illustrates their usefulness through application to some meteorological and seismic time series.     

零售商店绿色实践对顾客公民行为的影响研究

随着绿色发展成为国家战略,零售商店开展绿色实践是大势所趋,不仅对整个社会的绿色发展至关重要,也会对零售商店竞争力提升产生重要影响.然而,现今有关零售商店绿色实践方面的研究仍处于起步阶段,关于零售商店绿色实践对顾客公民行为影响的研究更为鲜见.文章根据亲社会行为理论、社会认同理论和精细加工理论构建零售商店绿色实践对顾客公民行为影响的概念模型,采用情境实验法进行实证检验.研究结果表明:零售商店的三类绿色实践均显著影响顾客公民行为,这种影响一方面是直接的,另一方面是通过顾客—商店认同间接作用于顾客公民行为,即顾客—商店认同在零售商店的三类绿色实践对顾客公民行为的影响路径中均起部分中介作用;顾客自我提升动机在零售商店的三类绿色实践对顾客—商店认同、顾客公民行为的影响路径中均具有正向调节作用.     

ADDITIVE MODELS WITH PREDICTORS SUBJECT TO MEASUREMENT ERROR

This paper develops a likelihood‐based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the presence of a structural measurement error model, the resulting likelihood involves intractable integrals, and a Monte Carlo expectation maximization strategy is developed for obtaining estimates. The method's performance is illustrated with a simulation study.     

恩施烟地土壤和烤烟团棵期硒含量与分布的研究

对湖北省恩施市几个主要产烟区的烟地土壤的调查分析和对烤烟盆栽试验结果的分析表明,烟地土壤含硒量因土壤母质而异,并与土壤有机碳含量呈正相关,其分布随土壤深度增加而减少;烤烟团棵期含硒量随土壤含硒量和土壤施硒量的增加而增加,并呈现出地下部分大于地上部分的分布规律。     

Understanding exponential smoothing via kernel regression

Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.     

《礼纬》、《乐纬》合论

《礼纬》、《乐纬》是纬书中的两种。由于古代乐学是从属于礼学的,因此,《礼纬》、《乐纬》可以放在一起研究。通过对西汉礼学的考察,可以知道《礼纬》、《乐纬》的形成与二戴礼学关系密切。在思想内容上,《礼纬》、《乐纬》主要记载了很多古代的礼乐制度和作用。《礼纬》、《乐纬》认为,礼乐在古代之所以重要并非因为它们的形式而在于它们观得失之效、反人道之正的作用。