Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Household debt and financial assets: evidence from Germany, Great Britain and the USA

Summary.  We explore the determinants of debt, financial assets and net worth at the household level by using survey data for Germany, Great Britain and the USA. To identify which households are potentially vulnerable to adverse changes in the economic environment, we also explore the determinants of a range of measures of financial pressure: the probability that a household has negative net worth; the debt-to-income ratio; mortgage income gearing; the saving-to-income ratio. Our empirical findings suggest that the poorest and the youngest households are the most vulnerable to adverse changes in their financial circumstances.     

Temporal pattern in number of staff on sick leave: the effect of downsizing

Summary.  The pattern of absenteeism in the downsizing process of companies is a topic in focus in economics and social science. A general question is whether employees who are frequently absent are more likely to be selected to be laid off or in contrast whether employees to be dismissed are more likely to be absent for the remaining time of their working contract. We pursue an empirical and microeconomic investigation of these theses. We analyse longitudinal data that were collected in a German company over several years. We fit a semiparametric transition model based on a mixture Poisson distribution for the days of absenteeism per month. Prediction intervals are considered and the primary focus is on the period of downsizing. The data reveal clear evidence for the hypothesis that employees who are to be laid off are more frequently absent before leaving the company. Interestingly, though, no clear evidence is seen that employees being selected to leave the company are those with a bad absenteeism profile.     

A general inverse sampling scheme and its application to adaptive cluster sampling

Not having a variance estimator is a seriously weak point of a sampling design from a practical perspective. This paper provides unbiased variance estimators for several sampling designs based on inverse sampling, both with and without an adaptive component. It proposes a new design, which is called the general inverse sampling design, that avoids sampling an infeasibly large number of units. The paper provide estimators for this design as well as its adaptive modification. A simple artificial example is used to demonstrate the computations. The adaptive and non‐adaptive designs are compared using simulations based on real data sets. The results indicate that, for appropriate populations, the adaptive version can have a substantial variance reduction compared with the non‐adaptive version. Also, adaptive general inverse sampling with a limitation on the initial sample size has a greater variance reduction than without the limitation.     

论北宋柳、苏词的雅俗对峙

雅与俗是一个分合消长的流变过程.词体文学从中、晚唐发展到北宋蔚为大观,至柳永与苏轼,先后出现了两种不同的创作趋向--雅与俗的对峙.这种对峙,有着各自不同的源流传承,浸染着宋代文人雅俗两极的文化个性,在词坛审美取向变革的背景作用下,显得尤为鲜明突出.它不仅完成了与东坡言志耆卿言情的构体开派,而且成为北宋词雅俗嬗变的关捩.     

Seemingly unrelated regression on the autoregressive (Ar(p)) singular equation system

This paper studies the estimation of seemingly unrelated regressions (SUR) of singular equation systems with an autoregressive error process (AR(p)) for each equation.Parameter estimates of the autoregressive singular equation system are not generally invariant to the equation deleted. Under the model specification restriction on the autoregressive parameters, the invariance property is preserved, and this paper shows that a single equation generalized least squares (GLS) estimation for a general autoregressive error process is equivalent to the SURGLS estimation of the AR(p) singular equation system.     

谢灵运研究中的两个问题

谢灵运袭封康乐公应在他父亲谢死后三年,谢死于太元十七年至二十一年间,灵运之袭封当在隆安年间,他十五岁时(隆安三年,399)由寄养的杜治回家,应当就是为了受封。关于谢灵运与刘宋王室的关系,近人多强调其矛盾的一面,其实刘裕、刘义隆两代皇帝对他都相当尊重和宽容,他之所以一再受到打击,一方面是由于他卷进了刘宋王朝内部的纷争,二则也是他个人的贵族作风有以致之。     

论刘勰文学批评的“文本”精神

刘勰的文学批评有别于先秦两汉时期的“以德为本”的文学批评。贯穿刘勰文学批评始终的是“以文为本”的精神,“文”是刘勰文学批评理论的核心,也是刘勰文学批评方法论依据。刘勰文学批评中的“文本”精神是我国文学批评走向自觉的重要标志。     

On calculating the fisher information in order statistics

This article gives a simple result for the expression of the Fisher information in order statistics. This result enables us to calculate easily the Fisher information in any set of order statistics whose details have been known to be messy and complicated. We consider here its application in the optimal spacing problem where the exact Fisher information in order statistics has been approximated with the asymptotic information or the reciprocal of the variance of a suitable estimator. This work was supported by Korea Research Foundation Grant(KRF-2000-015-DP0056)     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.