半参数广义线性模型惩罚似然估计的几个性质

本文在给出了半参数广义线性模型惩罚似然估计及惩罚准似然估计的基础上,得到了下面几个性质:(Ⅰ)极小惩罚似然估计及惩罚准似然估计等价于惩罚加权最小二乘估计;(Ⅱ)估计的收敛性及加速收敛法;(Ⅲ)估计的一种 Bayes 解释。     

光栅光调制器周期结构的优化分析与实验

针对光栅光调制器的光学调制特点,建立一种可变周期结构的光栅光调制器模型,采用Matlab软件中的数字图像处理方法进行研究。利用傅里叶变换分析两种周期结构的光栅光调制器在频谱面和像面的光场分布,并得到不同周期结构对器件调制对比度的影响。实验得到了两种结构像面的光强输出对比。结果表明:不同周期结构对器件的亮态调制效果影响不大;当器件上下反射面中心对齐,下层反射面在器件周期方向两侧各超出上层光栅面的1/2倍光栅间隙宽度时,器件具有最佳的暗态调制效果,从而得到了光栅光调制器的一种周期结构优化方案。     

Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes

Ecologists and conservationists need accurate and replicable tools for monitoring wetland conditions in order to develop and implement adaptive management strategies efficiently. The Rhone Delta (Camargue) in southern France encloses 9200 ha of fragmented reed marshes actively managed for reed harvesting, waterfowl hunting or cattle grazing, and holding significant numbers of vulnerable European birds. We used multi-season SPOT-5 data in conjunction with ground survey to assess the predictive power of satellite imagery in modelling indicators of reed structure (height, diameter, density and cover of green/dry stems) relevant to ecosystem management and bird ecology. All indicators could be predicted accurately with a combination of bands (SWIR, NIR) and indices (SAVI, OSAVI, NDWI, DVI, DVW, MSI) issued from scenes of March, June, July, September or December and subtraction between these. All models were robust when validated with an independent set of satellite and field data. The high spatial resolution of SPOT-5 scenes (pixel of 10 × 10 m) permits the monitoring of detailed attributes characterizing the reed ecosystem across a large spatial extent, providing a scientifically-based, replicable tool for managers, stakeholders and decision-makers to follow wetland conditions in the short and long-term. Combined with models on the ecological requirements of vulnerable bird species, these tools can provide maps of potential species ranges at spatial extents that are relevant to ecosystem functioning and bird populations.     

Approximate Predictive Densities and Their Applications in Generalized Linear Models

Exact calculations of model posterior probabilities or related quantities are often infeasible due to the analytical intractability of predictive densities. Here new approximations for obtaining predictive densities are proposed and contrasted with those based on the Laplace method. Our theory and a numerical study indicate that the proposed methods are easy to implement, computationally efficient, and accurate over a wide range of hyperparameters. In the context of GLMs, we show that they can be employed to facilitate the posterior computation under three general classes of informative priors on regression coefficients. A real example is provided to demonstrate the feasibility and usefulness of the proposed methods in a fully Bayes variable selection procedure.     

包含噪声因子二阶效应稳健设计响应面GLM研究

引入噪声因子线性作用的GLM(GeneralizedLinearModeling广义线性模型)能够处理传统响应面方法无法处理的由噪声因子线性作用导致的方差不等的情况,但是无法处理因噪声因子交互作用而导致方差不等的情况。通过引入噪声因子的二次效应及其之间的交互作用,将包含噪声因子线性作用的GLM推广到含有噪声因子二阶响应面的GLM,并给出推广模型的参数估计及其假设检验。     

动态特性稳健设计响应面GLM研究

通过在实验期间将噪声因子视为固定效应,对非齐方差的动态特性稳健设计响应面进行了研究。利用GLM(GeneralizedLinearModeling广义线性模型)对其期望模型和方差模型进行联合估计并给出所考察模型的参数估计及其假设检验方法。     

Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses

Joint modeling of multiple health related random variables is essential to develop an understanding for the public health consequences of an aging population. This is particularly true for patients suffering from multiple chronic diseases. The contribution is to introduce a novel model for multivariate data where some response variables are discrete and some are continuous. It is based on pair copula constructions (PCCs) and has two major advantages over existing methodology. First, expressing the joint dependence structure in terms of bivariate copulas leads to a computationally advantageous expression for the likelihood function. This makes maximum likelihood estimation feasible for large multidimensional data sets. Second, different and possibly asymmetric bivariate (conditional) marginal distributions are allowed which is necessary to accurately describe the limiting behavior of conditional distributions for mixed discrete and continuous responses. The advantages and the favorable predictive performance of the model are demonstrated using data from the Second Longitudinal Study of Aging (LSOA II).     

Traffic analysis zone level crash estimation models based on land use characteristics

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed. Demographic/socio-economic characteristics such as population, the number of household units and employment, traffic indicators such as trip productions and attractions, and, on-network characteristics such as center-lane miles by speed limit were observed to be correlated to land use characteristics, and, hence were not considered in the development of TAZ level crash estimation models. Urban residential commercial, rural district and mixed use district land use variables were observed to be correlated to other land use variables and were also not considered in the development of the models. Results obtained indicate that land use characteristics such as mixed use development, urban residential, single-family residential, multi-family residential, business and, office district are strongly associated and play a statistically significant role in estimating TAZ level crashes. The coefficient for single-family residential area was observed to be negative, indicating a decrease in the number of crashes with an increase in single-family residential area. Models were also developed to estimate these crashes by severity (injury and property damage only crashes). The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, and, to proactively apply safety treatments in high risk TAZs.     

Predicting fish species richness in estuaries: Which modelling technique to use?

Four different modelling techniques were compared and evaluated: generalized linear models (GLM), generalized additive models (GAM), classification and regression trees (CART) and boosted regression trees (BRT). Each method was used to model fish species richness variation throughout several Portuguese estuarine systems. Model comparisons were based on goodness-of-fit and predictive performance via cross-validation. The relative influence of the most important predictors according to each of the four models was also examined. Fitted BRT, CART, GAM and GLM models accounted for 70.6%, 57.0%, 34.6% and 23.7% of total model deviance, respectively. No single variable was consistently responsible for the larger amount of percentage of relative deviance explained by the models, but several variables were selected by the four models. Nevertheless, their relative importance was highly variable, according to each modelling technique. The tree-based models (CART and BRT) presented lower prediction errors after cross-validation. The limitations and usefulness of each technique are discussed.     

ROBUST FITTING OF INARCH MODELS

We discuss robust M‐estimation of INARCH models for count time series. These models assume the observation at each point in time to follow a Poisson distribution conditionally on the past, with the conditional mean being a linear function of previous observations. This simple linear structure allows us to transfer M‐estimators for autoregressive models to this situation, with some simplifications being possible because the conditional variance given the past equals the conditional mean. We investigate the performance of the resulting generalized M‐estimators using simulations. The usefulness of the proposed methods is illustrated by real data examples.