核偏最小二乘算法的图像超分辨率算法

提出了基于核偏最小二乘算法(KPLS)回归的超分辨率复原算法。该算法首先将高低分辨率图像块的高频信息和中频信息作为建立回归关系的特征,并对图像进行分块;依据相应的高低分辨率图像块的关系,使用KPLS建立起回归模型;在复原时,依据该模型回归得到高分辨率的图像块,将图像块拼接为高分辨率的图像。通过对人脸图像和车牌图像的实验结果,表明该算法无论是对人脸图像还是车牌图像都能取得较好的复原效果。     

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Summary.  Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.     

Restricted minimax robust designs for misspecified regression models

The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression.     

一种基于曲线拟合的客户预期贡献计量方法

本文对客户资产中最为关键的计算因子——客户预期贡献,提出一种利用最小二乘法进行回归分析,拟合出客户预期贡献的计算函数,并将其运用到客户资产计算公式中,建立客户资产度量模型.本文还以中国建设银行某支行餐饮娱乐业固定资产贷款业务为案例,阐明了该方法的应用,并对计算出的客户资产结果进行了拟合优度检验和显著性检验.     

A Multiple Imputation Approach to Linear Regression with Clustered Censored Data

Weextend Wei and Tanner's (1991) multiple imputation approach insemi-parametric linear regression for univariate censored datato clustered censored data. The main idea is to iterate the followingtwo steps: 1) using the data augmentation to impute for censoredfailure times; 2) fitting a linear model with imputed completedata, which takes into consideration of clustering among failuretimes. In particular, we propose using the generalized estimatingequations (GEE) or a linear mixed-effects model to implementthe second step. Through simulation studies our proposal comparesfavorably to the independence approach (Lee et al., 1993), whichignores the within-cluster correlation in estimating the regressioncoefficient. Our proposal is easy to implement by using existingsoftwares.     

Recovering Information from Incremental Responses

Some yields analysed and reported in the literature have been adjusted by subtracting a control. It is found that full information can be recovered for estimable parameters and the error variance using these incremental responses, in comparison with unadjusted data. These findings are of practical importance, and they supplement materials usually found in a graduate course in linear inference. The issues are illustrated using a case study from the literature.     

A Weighted Least Squares Approach to Levene's Test of Homogeneity of Variance

In 1960 Levene suggested a potentially robust test of homogeneity of variance based on an ordinary least squares analysis of variance of the absolute values of mean-based residuals. Levene's test has since been shown to have inflated levels of significance when based on the F-distribution, and tests a hypothesis other than homogeneity of variance when treatments are unequally replicated, but the incorrect formulation is now standard output in several statistical packages. This paper develops a weighted least squares analysis of variance of the absolute values of both mean-based and median-based residuals. It shows how to adjust the residuals so that tests using the F -statistic focus on homogeneity of variance for both balanced and unbalanced designs. It shows how to modify the F -statistics currently produced by statistical packages so that the distribution of the resultant test statistic is closer to an F-distribution than is currently the case. The weighted least squares approach also produces component mean squares that are unbiased irrespective of which variable is used in Levene's test. To complete this aspect of the investigation the paper derives exact second-order moments of the component sums of squares used in the calculation of the mean-based test statistic. It shows that, for large samples, both ordinary and weighted least squares test statistics are equivalent; however they are over-dispersed compared to an F variable.     

Asymptotic Bias in Parameter Estimation of AR-Processes Using Recursive Least Squares with Exponential Forgetting

The recursive least squares technique is often extended with exponential forgetting as a tool for parameter estimation in time-varying systems. The distribution of the resulting parameter estimates is, however, unknown when the forgetting factor is less than one. In this paper an approximative expression for bias of the recursively obtained parameter estimates in a time-invariant AR( n_a ) process with arbitrary noise is given, showing that the bias is non-zero and giving bounds on the approximation errors. Simulations confirm the approximation expressions.     

ADE-4: a multivariate analysis and graphical display software

We present ADE-4, a multivariate analysis and graphical display software. Multivariate analysis methods available in ADE-4 include usual one-table methods like principal component analysis and correspondence analysis, spatial data analysis methods (using a total variance decomposition into local and global components, analogous to Moran and Geary indices), discriminant analysis and within/between groups analyses, many linear regression methods including lowess and polynomial regression, multiple and PLS (partial least squares) regression and orthogonal regression (principal component regression), projection methods like principal component analysis on instrumental variables, canonical correspondence analysis and many other variants, coinertia analysis and the RLQ method, and several three-way table (k-table) analysis methods. Graphical display techniques include an automatic collection of elementary graphics corresponding to groups of rows or to columns in the data table, thus providing a very efficient way for automatic k-table graphics and geographical mapping options. A dynamic graphic module allows interactive operations like searching, zooming, selection of points, and display of data values on factor maps. The user interface is simple and homogeneous among all the programs; this contributes to making the use of ADE-4 very easy for non- specialists in statistics, data analysis or computer science.     

资源环境约束下中国TFP增长的空间差异和影响因素

运用DDF模型和ML生产率指数,对中国分省资源环境约束下的区域全要素生产率进行测算,采用Theil指数测算资源环境约束下中国全要素生产率增长的地区差异并按照多种空间尺度进行区域分解;构建空间面板数据模型,采用广义空间面板自回归最小二乘法对资源环境约束下全要素生产率增长的影响因素进行实证研究。研究结果表明,在2010年之前,资源环境约束下中国全要素生产率增长的空间差异总体上呈下降态势,地区内而非地区间差距是造成总体空间差异的主要来源。空间计量模型回归估计结果表明,资源环境约束下全要素生产率增长存在显著的正向空间溢出效应,经济发展水平、贸易开放和科技创新水平对资源环境约束下全要素生产率增长存在显著的促进作用,产业结构、能源结构和要素禀赋结构等结构因素对全要素生产率增长存在显著的负向影响,外商直接投资和环境规制水平对全要素生产率增长的影响在统计上并不显著。