主成分综合评价法的误区识别及其改进

大部分基于主成分分析的综合评价忽略了该方法排名结果不唯一的问题。本文通过理论证明了用多个主成分加权综合得到的指标进行排序一定是不可取的,进一步给出了用第一主成分进行综合评价的限制条件,指出了该评价法的存在误区的本质原因,使其应用更加规范和合理。在此基础上提出一种基于非线性投影的对数主成分评价法,改进了传统主成分分析的“线性化”约束,并从理论基础、几何意义和适用范围等方面阐述了本算法,还对2012年联合国人类发展指数的各评价方法进行分析和对比,验证了该方法的合理性和有效性。     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

Macroeconomic forecasting using approximate factor models with outliers

In this paper we consider estimating an approximate factor model in which candidate predictors are subject to sharp spikes such as outliers or jumps. Given that these sharp spikes are assumed to be rare, we formulate the estimation problem as a penalized least squares problem by imposing a norm penalty function on those sharp spikes. Such a formulation allows us to disentangle the sharp spikes from the common factors and estimate them simultaneously. Numerical values of the estimates can be obtained by solving a principal component analysis (PCA) problem and a one-dimensional shrinkage estimation problem iteratively. In addition, it is easy to incorporate methods for selecting the number of common factors in the iterations. We compare our method with PCA by conducting simulation experiments in order to examine their finite-sample performances. We also apply our method to the prediction of important macroeconomic indicators in the U.S., and find that it can deliver performances that are comparable to those of the PCA method.     

Copyright Damages and Statistics

This paper discusses the statistical issues that arise in conducting an economic damages analysis in the context of a litigation matter involving copyrights. Calculating damages in copyright cases turns out to be a natural application for econometric modelling methods. Surprisingly, elementary statistical issues can be a source of significant debate between the experts in such matters. In this paper, we present a case study and illustrate how issues such as interpretation of p -values and what "rejection of the null hypothesis" really "means" in such matters.     

Computation and analysis of multiple structural change models

In a recent paper, Bai and Perron ( 1998 ) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least‐squares operations of order O(T²) for any number of breaks. Our method can be applied to both pure and partial structural change models. Second, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program. Copyright © 2002 John Wiley & Sons, Ltd.     

An Application of Principal Component Analysis on Multivariate Time-stationary Spatio-temporal Data

Abstract

Principal component analysis (PCA) denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers PCA into the spatio-temporal realm. Our approach, named spatio-temporal principal component analysis (stPCA), allows for dimension reduction in the attribute space while striving to preserve much of the data's variance and maintaining the data's original structure in the spatio-temporal domain. Additionally to spatial autocorrelation stPCA exploits any serial correlation present in the data and consequently takes advantage of all particular features of spatial-temporal data. A simulation study underlines the superior performance of stPCA if compared to the original PCA or its spatial variants and an application on indicators of economic deprivation and urbanism demonstrates its suitability for practical use.     

浅析计算机网络系统集成技术方法

目前,基于计算机网络的系统集成技术方法主要有数据集成、API集成和方法集成三种形式。同时,不同的计算机网络对系统集成程度和模式要求也各不相同,因此,为了更好地阐述计算机网络系统集成技术方法,本文将结合普通高校校园网,简单分析系统集成技术方法,以此对网络系统集成技术方法的应用做进一步的探讨。     

Inference of Dynamic Generalized Linear Models: On-Line Computation and Appraisal

The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.     

Graphical analysis and financial classification: A case study

This paper illustrates the use of graphical analysis as a complementary diagnostic tool in financial classification problems. For more than two decades statistical models have been used frequently to understand the information content of multivariate data in the context of financial classification. These statistical classification models can be complemented by the use of computer-generated multidimensional data displays and graphical analysis. We illustrate the use of the scatterplot matrix, which is the simplest and very effective form of graphical analysis on a sample of bankrupt and non-bankrupt firms. The paper demonstrates the complementary nature of the scatterplot matrix for tree-structured classification models.     

Modeling Item Nonresponse in Questionnaires

The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.     

A methodology for the assessment of ridesharing program benefits and impacts

Due to the increased cost and decreased availability of energy, many cities are developing experimental ridesharing programs. The evaluation of such public transportation programs often involves some form of benefit/cost analysis. In this paper we review a methodology proposed by Wagner for calculating ridesharing program benefits. We propose a modification for calculating vehicle-miles reduction and suggest a survey instrument for collecting the appropriate data. In addition, we present a surrogate measure which is simpler to compute and allows sensitivity analysis for incorporating certain intangible factors. Data from a survey conducted in the Greater Cincinnati area is used to illustrate the calculations. The results can be useful in evaluating other ridesharing programs.     

Print versus electronic surveys: a comparison of two data collection methodologies

This paper compares the responses of consumers who submitted answers to a survey instrument focusing on Internet purchasing patterns both electronically and using traditional paper response methods. We present the results of a controlled experiment within a larger data collection effort. The same survey instrument was completed by 416 Internet customers of a major office supplies company, with approximately 60% receiving the survey in paper form and 40% receiving the electronic version. In order to evaluate the efficacy of electronic surveys relative to traditional, printed surveys we conduct two levels of analysis. On a macro-level, we compare the two groups for similarity in terms of fairly aggregate, coarse data characteristics such as response rates, proportion of missing data, scale means and inter-item reliability. On a more fine-grained, micro-level, we compare the two groups for aspects of data integrity such as the presence of data runs and measurement errors. This deeper, finer-grained analysis allows an examination of the potential benefits and flaws of electronic data collection.Our findings suggest that electronic surveys are generally comparable to print surveys in most respects, but that there are a few key advantages and challenges that researchers should evaluate. Notably, our sample indicates that electronic surveys have fewer missing responses and can be coded/presented in a more flexible manner (namely, contingent coding with different respondents receiving different questions depending on the response to earlier questions) that offers researchers new capabilities.     

Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?

Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming data and/or massive data. Despite the wide availability of recursive algorithms that can efficiently update the PCA when new data are observed, the literature offers little guidance on how to select a suitable algorithm for a given application. This paper reviews the main approaches to online PCA, namely, perturbation techniques, incremental methods and stochastic optimisation, and compares the most widely employed techniques in terms statistical accuracy, computation time and memory requirements using artificial and real data. Extensions of online PCA to missing data and to functional data are detailed. All studied algorithms are available in the  package onlinePCA on CRAN.     

Exploratory data analysis as a foundation of inductive research

Across academic disciplines, scientific progress is maximized when there is a balance between deductive and inductive approaches. To promote this balance in organizational science, rigorous inductive research aimed at phenomenon detection must be further encouraged. To this end, the present article discusses the logic and methods of exploratory data analysis (EDA), the mode of analysis concerned with discovery, exploration, and empirically detecting phenomena in data. We begin by first describing the historical and conceptual background of EDA. We then discuss two issues related to EDA and its relationship to scientific credibility. First, we argue that EDA fosters a replication-based science by requiring cross-validation and by emphasizing the natural uncertainty of data patterns. Second, we clarify that EDA is distinguishable from other exploratory practices that are considered scientifically questionable (e.g., “p-hacking”, “data fishing” and “data-dredging”). In the following section of the paper, we present a final argument for EDA: that it helps maximize the value of data. To illustrate this point, we present several graphical methods for detecting data patterns and provide references to further techniques for the interested reader.     

Masking identification of discrete choice models under simulation methods

We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact, are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation, we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.     

A synthesis of exact inferential results for exponential step-stress models and associated optimal accelerated life-tests

A reliability experimenter is often interested in studying the effects of extreme or varying stress factors such as load, pressure, temperature and voltage on the lifetimes of experimental units. Accelerated life-tests allow the experimenter to vary the levels of these stress factors in order to obtain information on the parameters of the lifetime distributions more rapidly than under normal operating conditions. Step-stress tests are a particular class of accelerated life-tests which allow the experimenter to change the stress levels at pre-fixed times during the life-testing experiment. One of the prominent models assumed in step-stress tests is the cumulative exposure model which connects the lifetime distribution of units at one stress level to the lifetime distributions at preceding stress levels. Under such a cumulative exposure model and the assumption that the lifetimes at different stress levels are exponentially distributed, we review in this article various developments on exact inferential methods for the model parameters based on different forms of censored data. We also describe the approximate confidence intervals based on the asymptotic properties of maximum likelihood estimators as well as the bootstrap confidence intervals, and provide some comparisons between these methods. Finally, we present some examples to illustrate all the inferential methods discussed here.     

Estimating dynamic local interactions models

This paper presents empirical methods for studying a class of local interactions models in which agents’ transitions are affected by their neighbors’ states. We consider an application to urban unemployment and social networks in job search using publicly available cross-section and retrospective data. Most links in our model are local, but some span an entire metropolitan area. Our methods are designed to accommodate the presence of strong cross-sectional dependence arising from these few cross-metro-area links. We also present simple methods to compare data and model spell distributions and to illustrate the model's dynamic properties.     

Stability and integrity tests in data envelopment analysis

Studies that apply data envelopment analysis often neglect testing the stability of the efficient frontier to data perturbations, and, to a lesser extent, the ability of efficiency scores to correctly discriminate between units on performance (integrity). Our primary motivation is to demonstrate methods that can help reduce the number of managerial decisions based on results that may be unreliable. To this end, we illustrate multiple tests of stability and integrity in an environment of fully units-invariant efficiency measurement. This application of tests of stability and integrity using a slacks-based measure of efficiency is the first in a peer-reviewed journal.     

Nonparametric checks for varying coefficient models with missing response at random

In this paper, we propose a test on the parametric form of the coefficient functions in the varying coefficient model with missing response. Two groups of completed data sets are constructed by using imputation and inverse probability weighting methods respectively. By noting that the coefficient part can be a regression function for a specific model, we construct two empirical process-based tests. The asymptotical distributions of the proposed tests under null and local alternative hypothesis are investigated respectively. Simulation study is carried out to show the power performance of the test. We illustrate the proposed approaches with an environmental data set.     

服务业企业标准化工作探析

标准化是服务业企业服务、经营、管理活动的基础,是创造良好服务秩序的重要保证。目前,在国家大力发展服务业,促进经济转型的重要时期,深人领会标准化的精髓,结合企业实际,合理组织标准化工作,运用标准化方法,对企业的健康发展至关重要。文章分析了标准化工作对服务业企业发展的重要作用,提出了标准化工作管理的职能,阐述了标准化的工作内容,对企业开展标准化工作、提升核心竞争力具有重要的意义。