Generalized linear models with functional predictors

Summary. We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.     

Bayes model averaging with selection of regressors

Summary. When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This can greatly aid the interpretation of the model. It also reduces the cost if measured variables have costs. The development here uses decision theory in the context of the multivariate general linear model. In passing, this reduced predictor space Bayes model averaging is contrasted with single-model approximations. A fast algorithm for updating regressions in the Markov chain Monte Carlo searches for posterior inference is developed, allowing many more variables than observations to be contemplated. We discuss the merits of absolute rather than proportionate shrinkage in regression, especially when there are more variables than observations. The methodology is illustrated on a set of spectroscopic data used for measuring the amounts of different sugars in an aqueous solution.     

Bandwidth selection for local linear regression smoothers

Summary. The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.     

夸克胶子等离子体动力论研究的近期进展

介绍了夸克胶子等离子体（ＱＧＰ）的动力论基础,讨论了在半经典近似下的ＱＧＰ输运方程的阿贝尔优势近似和线性近似,指出了超越阿贝尔优势和线性近似的可能途径     

汽轮发电机转子静挠度的多元统计分析

讨论了如何用多元线性回归分析的统计方法与有限元法相结合,计算发电机组转子的热应变、基础负载沉降及支承预诸因素影响下的静挠曲线。     

除环上一个矩阵秩的恒等式的修正

本文修正了除环上一个矩阵秩的恒等式，推广、改进了屠伯项教授的相应结果．     

一部具有强烈时代气息的理论巨著──评林文益教授主编的《贸易经济学》

一部具有强烈时代气息的理论巨著──评林文益教授主编的《贸易经济学》贾履让由中国人民大学贸易经济系教授、博士生导师林文益主编的《贸易经济学》一书，已于１９９５年由中国财政经济出版社出版。这是一本适用于贸易经济专业本科生、硕士生乃至博士生所需要的基础性理...     

有非负约束的线性模型

讨论有非负约束的线性模型的估计，证明了此模型可以用矩阵的消去变换来求解，主要应用于可靠性统计分析中。     

光度法同时测定磷和硅的研究

用钼蓝法分别测定磷和硅的含量多有报导［１］［２］［３］，但用该法对其进行同时测定未见报导．本文探讨了用钼蓝法同时测定磷和硅，确定了其测定条件．本法具有简便、快速、不需特殊试剂等特点，可用于水等试样中磷和硅的同时测定．     

二乙酸甲胺磷合锰的合成研究

甲胺磷是一种广谱性有机磷杀虫剂，甲胺磷的金属配位化合物具有生物活性、作为农药缓释剂，本文研究了二乙酸甲胺磷合锰配位化合物的合成方法、分子的组成、磁性、电子自旋、配合物的成键情况等．