The Modal Age of Statistics

Recently, a number of statistical problems have found an unexpected solution by inspecting them through a ‘modal point of view'. These include classical tasks such as clustering or regression. This has led to a renewed interest in estimation and inference for the mode. This paper offers an extensive survey of the traditional approaches to mode estimation and explores the consequences of applying this modern modal methodology to other, seemingly unrelated, fields.     

Extrapolation‐based Bandwidth Selectors: A Review and Comparative Study with Discussion on Bivariate Applications

Cross‐validation is a widely used tool in selecting the smoothing parameter in a non‐parametric procedure. However, it suffers from large sampling variation and tends to overfit the data set. Many attempts have been made to reduce the variance of cross‐validation. This paper focuses on two recent proposals of extrapolation‐based cross‐validation bandwidth selectors: indirect cross‐validation and subsampling‐extrapolation technique. In univariate case, we notice that using a fixed value parameter surrogate for indirect cross‐validation works poorly when the true density is hard to estimate, while the subsampling‐extrapolation technique is more robust to non‐normality. We investigate whether a hybrid bandwidth selector could benefit from the advantages of both approaches and compare the performance of different extrapolation‐based bandwidth selectors through simulation studies, real data analyses and large sample theory. A discussion on their extension to bivariate case is also presented.     

Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review

The most common way for treating item non‐response in surveys is to construct one or more replacement values to fill in for a missing value. This process is known as imputation. We distinguish single from multiple imputation. Single imputation consists of replacing a missing value by a single replacement value, whereas multiple imputation uses two or more replacement values. This article reviews various imputation procedures used in National Statistical Offices as well as the properties of point and variance estimators in the presence of imputed survey data. It also provides the reader with newer developments in the field.     

基于均值聚类的背景估计算法

本文提出了基于均值聚类的背景估计算法。首先利用提取特征点,然后利用K-means聚类算法去除错误的特征点对。最后利用随机样本一致(RANSAC)算法和最小二乘方法求解运动参数。实验结果表明本文算法比原始算法的峰值信噪比提高。该算法能更加准确的实现运动背景估计,提高了运动背景估计的鲁棒性,同时提高了计算速度。     

Statistical inference on seemingly unrelated non‐parametric regression models with serially correlated errors

This article is concerned with the inference on seemingly unrelated non‐parametric regression models with serially correlated errors. Based on an initial estimator of the mean functions, we first construct an efficient estimator of the autoregressive parameters of the errors. Then, by applying an undersmoothing technique, and taking both of the contemporaneous correlation among equations and serial correlation into account, we propose an efficient two‐stage local polynomial estimation for the unknown mean functions. It is shown that the resulting estimator has the same bias as those estimators which neglect the contemporaneous and/or serial correlation and smaller asymptotic variance. The asymptotic normality of the resulting estimator is also established. In addition, we develop a wild block bootstrap test for the goodness‐of‐fit of models. The finite sample performance of our procedures is investigated in a simulation study whose results come out very supportive, and a real data set is analysed to illustrate the usefulness of our procedures.     

A comment on Mendeş and Yiğit (2013), ‘Type I error and test power of different tests for testing interaction effects in factorial experiments’, Statistica Neerlandica, 67:1–26

In their advocacy of the rank‐transformation (RT) technique for analysis of data from factorial designs, Mende? and Yi?it (Statistica Neerlandica, 67, 2013, 1–26) missed important analytical studies identifying the statistical shortcomings of the RT technique, the recommendation that the RT technique not be used, and important advances that have been made for properly analyzing data in a non‐parametric setting. Applied data analysts are at risk of being misled by Mende? and Yi?it, when statistically sound techniques are available for the proper non‐parametric analysis of data from factorial designs. The appropriate methods express hypotheses in terms of normalized distribution functions, and the test statistics account for variance heterogeneity.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Adaptive Estimation of a Conditional Density

The authors consider the problem of estimating a conditional density by a conditional kernel density estimate when the error associated with the estimate is measured by the L₁‐norm. On the basis of the combinatorial method of Devroye and Lugosi ( 1996 ), they propose a method for selecting the bandwidths adaptively and for providing a theoretical justification of the approach. They use simulated data to illustrate the finite‐sample performance of their estimator.     

Extreme Value Theory and Statistics of Univariate Extremes: A Review

Statistical issues arising in modelling univariate extremes of a random sample have been successfully used in the most diverse fields, such as biometrics, finance, insurance and risk theory. Statistics of univariate extremes (SUE), the subject to be dealt with in this review paper, has recently faced a huge development, partially because rare events can have catastrophic consequences for human activities, through their impact on the natural and constructed environments. In the last decades, there has been a shift from the area of parametric SUE, based on probabilistic asymptotic results in extreme value theory, towards semi‐parametric approaches. After a brief reference to Gumbel's block methodology and more recent improvements in the parametric framework, we present an overview of the developments on the estimation of parameters of extreme events and on the testing of extreme value conditions under a semi‐parametric framework. We further discuss a few challenging topics in the area of SUE. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Developments in Survey Research over the Past 60 Years: A Personal Perspective

Many developments have occurred in the practice of survey sampling and survey methodology in the past 60 years or so. These developments have been partly driven by the emergence of computers and the continuous growth in computer power over the years and partly by the increasingly sophisticated demands from the users of survey data. The paper reviews these developments with a main emphasis on survey sampling issues for the design and analysis of social surveys. Design‐based inference based on probability samples was the predominant approach in the early years, but over time, that predominance has been eroded by the need to employ model‐dependent methods to deal with missing data and to satisfy analysts' demands for survey estimates that cannot be met with design‐based methods. With the continuous decline in response rates that has occurred in recent years, much current research has focused on the use of non‐probability samples and data collected from administrative records and web surveys.     

Adjusting for Non‐Response in Population‐Based Case‐Control Studies

In this paper we discuss the analysis of data from population‐based case‐control studies when there is appreciable non‐response. We develop a class of estimating equations that are relatively easy to implement. For some important special cases, we also provide efficient semi‐parametric maximum‐likelihood methods. We compare the methods in a simulation study based on data from the Women's Cardiovascular Health Study discussed in Arbogast et al. (Estimating incidence rates from population‐based case‐control studies in the presence of non‐respondents, Biometrical Journal 44, 227–239, 2002).     

低功耗分簇路由算法LEACH的能耗分析

文章对无线传感器网络低功耗分簇路由协议的代表性算法—LEACH的运行机制以及性能做了详细的研究,针对该算法的分簇阶段、簇的建立阶段以及稳定的数据传输阶段的相关原理和运行情况作了深入分析。最后从正反两方面总结了LEACH协议的运行特性。     

Remarks on the strong law of large numbers for a triangular array of associated random variables

A strong law of large numbers for a triangular array of strictly stationary associated random variables is proved. It is used to derive the pointwise strong consistency of kernel type density estimator of the one-dimensional marginal density function of a strictly stationary sequence of associated random variables, and to obtain an improved version of a result by Van Ryzin (1969) on the strong consistency of density estimator for a sequence of independent and identically distributed random variables.     

Spatially Balanced Sampling: A Review and A Reappraisal

Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design‐based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations.     

An Updated Literature Review of Distance Correlation and Its Applications to Time Series

The concept of distance covariance/correlation was introduced recently to characterise dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function, and we demonstrate its applicability to time series analysis. We will see that the auto‐distance covariance/correlation function is able to identify non‐linear relationships and can be employed for testing the i.i.d. hypothesis. Comparisons with other measures of dependence are included.     

Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour

A rich theory of production and analysis of productive efficiency has developed since the pioneering work by Tjalling C. Koopmans and Gerard Debreu. Michael J. Farrell published the first empirical study, and it appeared in a statistical journal (Journal of the Royal Statistical Society), even though the article provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analysing productivity and efficiency of firms. Both parametric and non‐parametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue or profit frontiers, which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labour, energy and capital) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This ‘guided tour’ reviews the development of various non‐parametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

TOTAL FACTOR PRODUCTIVITY ESTIMATION: A PRACTICAL REVIEW

Abstract This paper aims to provide empirical researchers with an overview of the methodological issues that arise when estimating total factor productivity at the establishment level, as well as of the existing (parametric and semi‐parametric) techniques designed to overcome them. Apart from the well‐known simultaneity and selection bias, attention is given to methodological issues that have emerged more recently and that are related to the use of deflated values of inputs and outputs (as opposed to quantities) in estimating productivity at the firm level, as well as to the endogeneity of product choice. In discussing the estimation procedures applied in the literature, attention is given to recent developments in the field. Using data on single‐product firms active in the Belgian food and beverages sector, the most commonly applied estimators are illustrated, allowing for comparison of the obtained productivity estimates by way of a simple evaluation exercise.     

On Pooling of Data and Its Relative Efficiency

Pooling of data is often carried out to protect privacy or to save cost, with the claimed advantage that it does not lead to much loss of efficiency. We argue that this does not give the complete picture as the estimation of different parameters is affected to different degrees by pooling. We establish a ladder of efficiency loss for estimating the mean, variance, skewness and kurtosis, and more generally multivariate joint cumulants, in powers of the pool size. The asymptotic efficiency of the pooled data non‐parametric/parametric maximum likelihood estimator relative to the corresponding unpooled data estimator is reduced by a factor equal to the pool size whenever the order of the cumulant to be estimated is increased by one. The implications of this result are demonstrated in case–control genetic association studies with interactions between genes. Our findings provide a guideline for the discriminate use of data pooling in practice and the assessment of its relative efficiency. As exact maximum likelihood estimates are difficult to obtain if the pool size is large, we address briefly how to obtain computationally efficient estimates from pooled data and suggest Gaussian estimation and non‐parametric maximum likelihood as two feasible methods.     

浅谈工程项目管理模式与工程承发包模式的区别

针对目前将工程项目管理模式与工程承发包模式混淆的状况,阐述了工程项目管理模式及工程承发包模式的涵义,分析了两者的关系以及对业主项目组织结构的影响.     

A Fuzzy Clustering Algorithm Based on the Analogy with Mechanical Physics: MeccFuz

This paper presents a new algorithm of fuzzy clustering, based on analogy withmechanics physics. The vectors x_i are compared with `material points' and the clusters can be looked on as material points clouds, that are characterized byspecific units. The choice is based on a study of potential energy. Once a specificunit is chosen, we calculated the membership of the other units whit respect it.A study of a sociology test is performed and the results are very encouraging,though an further deepening is needed like a thermodynamics analogy application.