Doubly periodic non-homogeneous Poisson models for hurricane data

Non-homogeneous Poisson processes with periodic claim intensity rate have been proposed as claim counts in risk theory. Here a doubly periodic Poisson model with short- and long-term trends is studied. Beta-type intensity functions are presented as illustrations. The likelihood function and the maximum likelihood estimates of the model parameters are derived.Doubly periodic Poisson models are appropriate when the seasonality does not repeat exactly the same short-term pattern every year, but has a peak intensity that varies over a longer period. This reflects periodic environments like those forming hurricanes, in alternating El Niño/La Niña years. An application of the model to the data set of Atlantic hurricanes affecting the United States (1899–2000) is discussed in detail.     

行为金融学理论述评

20世纪80年代以来,大量的金融研究与市场异象对传统资本市场理论提出了挑战。行为金融理论在整合多学科研究成果,特别是心理学研究的基础上,从投资者的实际决策行为出发,分析与解释金融市场。通过文献研究,文章回顾了有关行为金融学的基本理论与研究,包括前景理论及其他相关理论的发展。此外,对国内有关行为金融学研究特点进行总结分析,并对其今后研究发展提出了设想。     

非负矩阵谱半径的新估计

对于非负矩阵的谱半径进行研究,分析了文[1,2]中的结果,给出了非负矩阵谱半径的新估计,该结果改进了文[1,2]中的相关结果.     

Self-weighted least absolute deviation estimation for infinite variance autoregressive models

Summary.  How to undertake statistical inference for infinite variance autoregressive models has been a long-standing open problem. To solve this problem, we propose a self-weighted least absolute deviation estimator and show that this estimator is asymptotically normal if the density of errors and its derivative are uniformly bounded. Furthermore, a Wald test statistic is developed for the linear restriction on the parameters, and it is shown to have non-trivial local power. Simulation experiments are carried out to assess the performance of the theory and method in finite samples and a real data example is given. The results are entirely different from other published results and should provide new insights for future research on heavy-tailed time series.     

Semiparametric mixture models and repeated measures: the multinomial cut point model

Summary.  Suppose that we have m repeated measures on each subject, and we model the observation vectors with a finite mixture model.  We further assume that the repeated measures are conditionally independent. We present methods to estimate the shape of the component distributions along with various features of the component distributions such as the medians, means and variances. We make no distributional assumptions on the components; indeed, we allow different shapes for different components.     

软件开发项目工作量估算技术的比较研究

软件开发项目的工作量估算技术一般分为三类:基于专家判断的技术、基于算法模型的技术和面向学习的技术.不同的估算技术各有自己的优点和局限性,没有一种估算技术能适用于所有开发环境,并且软件开发方法和技术的更新速度也对所有这些估算技术提出了挑战.软件组织应根据具体的项目特征和可获得的信息来选择合适的估算技术,并针对当前项目情况对使用的估算模型加以调整,依据不同技术的特点组合不同的估算技术进行估算,以提高估算准确性.在估算项目工作量时要充分考虑到项目前期阶段的工作量,并建立本组织的软件项目库.     

Estimation of Polygons and Areas

Abstract.  In forestry the problem of estimating areas is central. This paper addresses area estimation through fitting of a polygon to observed coordinate data. Coordinates of corners and points along the sides of a simple closed polygon are measured with independent random errors. This paper focuses on procedures to adjust the coordinates for estimation of the polygon and its area. Different new techniques that consider different amounts of prior information are described and compared. The different techniques use restricted least squares, maximum likelihood and the expectation maximization algorithm. In a simulation study it is shown that the root mean square errors of the estimates are decreased when coordinates are adjusted before estimation. Minor further improvement is achieved by using prior information about the order and the distribution of the points along the sides of the polygon. This paper has its origin in forestry but there are also other applications.     

Penalized contrast estimator for adaptive density deconvolution

The authors consider the problem of estimating the density g of independent and identically distributed variables X_I, from a sample Z₁,… Z_n such that Z_I = X_I + σ? for i = 1,…, n, and E is noise independent of X, with σ? having a known distribution. They present a model selection procedure allowing one to construct an adaptive estimator of g and to find nonasymptotic risk bounds. The estimator achieves the minimax rate of convergence, in most cases where lower bounds are available. A simulation study gives an illustration of the good practical performance of the method.     

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Abstract.  The Andersson–Madigan–Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced Lauritzen–Wermuth–Frydenberg Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.     

Integrated Square Error Asymptotics for Supersmooth Deconvolution

Abstract.  We derive the asymptotic distribution of the integrated square error of a deconvolution kernel density estimator in supersmooth deconvolution problems. Surprisingly, in contrast to direct density estimation as well as ordinary smooth deconvolution density estimation, the asymptotic distribution is no longer a normal distribution but is given by a normalized chi-squared distribution with 2 d.f. A simulation study shows that the speed of convergence to the asymptotic law is reasonably fast.