bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Behavioral Survey Methodologists in National Statistical
Authors: Dillman Don A. 
Published: May 2007

SummaryThe expertise of behavioral survey methodologists is needed in national statistical agencies because of the necessity of using theory and research from the social sciences to reduce survey error. In this paper various social science based explanations for measurement error and nonresponse error are described in order to illustrate the conceptual foundations of such error reduction efforts. Three roles for behavioral survey methodologists in national statistical agencies are then discussed. They include: 1) bring an error reduction perspective to bear in an influential way for all aspects of designing and implementing agency surveys; 2) bring theoretical efficiency and effectiveness to experimental tests of alternative questionnaire designs and implementation procedures through the use of theories, concepts and pretests and findings of past behavioral science research; and 3) contribute to the expanding science of survey methodology.
Robust Methods for the Analysis of Income Distribution, Inequality and Poverty
Authors: Victoria‐Feser Maria‐Pia 
Published: May 2007

SummaryIncome distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribution to decrease inequality, or to implement social policies to reduce poverty. The available data come mostly from surveys (and not censuses as it is often believed) and often subject to long debates about their reliability because the sources of errors are numerous. Moreover the forms in which the data are availabe is not always as one would expect, i.e. complete and continuous (microdata) but one also can only have data in a grouped form (in income classes) and/or truncated data where a portion of the original data has been omitted from the sample or simply not recorded.Because of these data features, it is important to complement classical statistical procedures with robust ones. In tis paper such methods are presented, especially for model selection, model fitting with several types of data, inequality and poverty analysis and ordering tools. The approach is based on theInfluence Function (IF)developed by Hampel (1974) and further developed by Hampel, Ronchetti, Rousseeuw & Stahel (1986). It is also shown through the analysis of real UK and Tunisian data, that robust techniques can give another picture of income distribution, inequality or poverty when compared to classical ones.
Women in the Statistics Profession: A Status Report
Authors: Carlson Beverley A. 
Published: May 2007

SummaryThe mandate of the International Statistical Institute (ISI) is to foster international statistical work and interchange among statisticians and countries. However, women are under‐represented in the ISI and this situation is a cause for concern in the Institute and its membership and particularly the ISI Committee on Women in Statistics (CWS). This paper analyses the representation of women in the ISI and in the statistics profession which is low in virtually every country, a pattern of generations. It looks at the diverse realities among countries and regions, and the broader context of women working in the scientific professions.
A Review of Statistical Methods for Genome Mapping
Authors: Jones Hywel B. 
Published: May 2007

SummaryFramework maps of the human genome are an important staging post in the on‐going effort to sequence the entire genome. The existence of high quality maps is also a prerequistite for studies attempting to determine the location of genes involved in common diseases. The basic experimental approaches to constructing both genetic and physical maps are briefly described as well as their respective uses. A variety of statistical approaches to map construction are outlined including parsimony, maximum likelihood and Bayesian methodologies. The mostly widely used of these, the method of maximum likelihood, is discussed in detail, particularly in the context of physical mapping using radiation hybrids. Finally, current statistical issues and problems in the field of genome mapping are described.
Linkage Disequilibrium Mapping of Disease Susceptibility Genes in Human Populations
Authors: Clayton David 
Published: May 2007

SummaryThe paper reviews recent work on statistical methods for using linkage disequilibrium to locate disease susceptibility genes, given a set of marker genes at known positions in the genome. The paper starts by considering a simple deterministic model for linkage disequilibrium and discusses recent attempts to elaborate it to include the effects of stochastic influences, of “drift”, by the use of either Writht‐Fisher models or by approaches based on the coalescence of the genealogy of the sample of disease chromosomes. Most of this first part of the paper concerns a series of diallelic markers and, in this case, the models so far proposed are hierarchical probability models for multivariate binary data. Likelihoods are intractable and most approaches to linkage disequilibrium mapping amount to marginal models for pairwise associations between individual markers and the disease susceptibility locus. Approaches to evalutation of a full likelihood require Monte Carlo methods in order to integrate over the large number of unknowns. The fact that the initial state of the stochastic process which has led to present‐day allele frequencies is unknown is noted and its implications for the hierarchical probability model is discussed. Difficulties and opportunities arising as a result of more polymorphic markers and extended marker haplotypes are indicated. Connections between the hierarchical modelling approach and methods based upon identity by descent and haplotype sharing by seemingly unrelated case are explored. Finally problems resulting from unknown modes of inheritance, incomplete penetrance, and “phenocopies” are briefly reviewed.
MCMC Estimation of Multi‐locus Genome Sharing and Multipoint Gene Location Scores
Authors: Thompson E. A. 
Published: May 2007

SummaryEffective linkage detection and gene mapping requires analysis of data jointly on members of extended pedigrees, jointly at multiple genetic markers. Exact likelihood computation is then often infeasible, but Markov chain Monte Carlo (MCMC) methods permit estimation of posterior probabilities of genome sharing among relatives, conditional upon marker data. In principle, MCMC also permits estimation of linkage analysis location score curves, but in practice effective MCMC samplers are hard to find. Although the whole‐meiosis Gibbs sampler (M‐sampler) performs well in some cases, for extended pedigrees and tightly linked markers better samplers are needed. However, using the M‐sampler as a proposal distribution in a Metropolis‐Hastings algorithm does allow genetic interference to be incorporated into the analysis.
On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees
Authors: Sheehan N. A. 
Published: May 2007

SummaryMarkov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications for the analysis of the mixed inheritance model, for example, where genetic variation is assumed to be due to one single locus of large effect and many loci each with a small effect. Similarly, reducibility arises in the detection of quantitative trait loci from incomplete discrete marker data. This paper aims to describe the estimation problem in terms of simple discrete genetic models and the single‐site Gibbs sampler. Reducibility of the Gibbs sampler is discussed and some current methods for circumventing the problem outlined.
Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
Authors: Lanterman Aaron D. 
Published: May 2007

SummaryInvestigators interested in model order estimation have tended to divide themselves into widely separated camps; this survey of the contributions of Schwarz, Wallace, Rissanen, and their coworkers attempts to build bridges between the various viewpoints, illuminating connections which may have previously gone unnoticed and clarifying misconceptions which seem to have propagated in the applied literature. Our tour begins with Schwarz's approximation of Bayesian integrals via Laplace's method. We then introduce the concepts underlying Rissanen's minimum description length principle via a Bayesian scenario with a known prior; this provides the groundwork for understanding his more complex non‐Bayesian MDL which employs a “universal” encoding of the integers. Rissanen's method of parameter truncation is contrasted with that employed in various versions of Wallace's minimum message length criteria. Rissanen's more recent notion of stochastic complexity is outlined in terms of Bernardo's information‐theoretic derivation of the Jeffreys prior.
Probabilistic Models for Bacterial Taxonomy
Authors: Gyllenberg M. , Koski T. 
Published: May 2007

SummaryWe give a survey of different partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way.
Ethnic, Religious and Language Groups: Towards a Set of Rules for Data Collection and Statistical Analysis
Authors: Haug Werner 
Published: May 2007

SummaryThe establishment of reliable and authoritative figures on ethnic, religious and language groups plays a prominent role in efforts to fight discrimination and foster individual human rights as well as minority rights. But how is an ethnic or religious group defined? Who belongs to it? How can we measure its characteristics? There is no consensus concerning these questions among statisticians, demographers and the social sciences at large. The article builds on the rich experience of Europe, including the multinational empires of the past. It identifies the background of diverging concepts and definitions, different data sources and measurement problems of ethnic, religious and language groups. The conclusion presents a set of rules and guidelines for the improvement of data collection, especially in the context population censuses and sample surveys, as well as for data analysis.
Meaningful Measures: Indicators on Progress, Progress on Indicators
Authors: de Vries Willem F.M. 
Published: May 2007

SummaryThis article reviews the international debate on statistical indicators. It describes historical backgrounds, as well as the present chaotic situation. It discusses what statistical indicators are and what they are for, as well as criteria for the choice of indicators, designed to monitor the achievement of economic, social, demographic, environmental and other goals set by United Nations conferences. Some suggestions for future work are made.
Uses, Abuses and Misuses of Significance Tests in the Scientific Community: Won't the Bayesian Choice be Unavoidable?
Authors: Lecoutre Bruno , Lecoutre Marie‐Paule , Poitevineau Jacques 
Published: May 2007

SummaryThe current context of the “significance test controversy” is first briefly discussed. Then experimental studies about the use of null hypothesis significance tests by scientific researchers and applied statisticians are presented. The misuses of these tests are reconsidered as judgmental adjustments revealing researchers’requirements towards statistical inference. Lastly alternative methods are considered. Consequently we automatically ask ourselves “won't the Bayesian choice be unavoidable?”
On Conformity Testing and the Use of two Stage Procedures
Authors: Holst Erik , Thyregod Poul , Wilrich Peter‐Th. 
Published: May 2007

SummaryConformity testing is a systematic examination of the extent to which an entity conforms to specified requirements. Such testing is performed in industry as well as in regulatory agencies in a variety of fields. In this paper we discuss conformity testing under measurement or sampling uncertainty. Although the situation has many analogies to statistical testing of a hypothesis concerning the unknown value of the measurand there are no generally accepted rules for handling measurement uncertainty when testing for conformity.Usually the objective of a test for conformity is to provide assurance of conformity. We therefore suggest that an appropriate statistical test for conformity should be devised such that there is only a small probability of declaring conformity when in fact the entity does not conform. An operational way of formulating this principle is to require that whenever an entity has been declared to be conforming, it should not be possible to alter that declaration, even if the entity was investigated with better (more precise) measuring instruments, or measurement procedures.Some industries and agencies designate specification limits under consideration of the measurement uncertainty. This practice is not invariant under changes of measurement procedure. We therefore suggest that conformity testing should be based upon a comparison of a confidence interval for the value of the measurand with some limiting values that have been designated without regard to the measurement uncertainty. Such a procedure is in line with the recently established practice of reporting measurement uncertainty as “an interval of values that could reasonably be attributed to the measurand”.The price to be paid for a reliable assurance of conformity is a relatively large risk that the procedure will fail to establish conformity for entities that only marginally conform. We suggest a two‐stage procedure that may improve this situation and provide a better discriminatory ability. In an example we illustrate the determination of the power function of such a two‐stage procedure.
The History of the Dirichlet and Liouville Distributions
Authors: Gupta Rameshwar D. , Richards Donald St. P. 
Published: May 2007

SummaryThe Dirichlet distributions have long been the subject of intense scrutiny in statistics and probability. Despite the enormous interest in, and wide‐ranging applications of, these distributions, little appears to be known about their history. In this article we review the development of the Dirichlet distributions and their companions, the Liouville distributions. After reviewing some integral formulas of Dirichlet and Liouville, we survey the theory and applications of these distributions in statistics.
Estimation Methods and Related Systems at Statistics Canada
Authors: Singh M.P , Hidiroglou M.A. , Gambino J.G. , Kovaçević M.S. 
Published: May 2007

SummaryThis paper provides an overview of research in estimation techniques, their application, and the development of generalized estimation systems at Statistics Canada. In Canada, the demand for more detailed and better quality cross‐sectional data related to various sodo‐economic issues has increased significantly in recent years. Also, there has been increasing interest in longitudinal data to better understand and interpret the relationships among variables, necessitating the implementation of a number of large scale panel surveys by Statistics Canada. The paper briefly discusses estimation for longitudinal data and a weighting approach developed for cross‐sectional data Prom these surveys. For cross‐sectional household and business surveys, as well as the census of population, appropriate dibration estimators developed for each situation are briefly discussed. In addition, regression composite estimation, a method developed to improve the quality of cross‐sectional estimates from rotating panel surveys such as the Canadian Labour Force Survey, is presented. With regard to more detailed cross‐sectional estimates at sub‐provincial levels, different approaches to small area estimation developed for various programs are also presented. We SUmmarize the various modules developed lor the GeneraIiized Ektimation System. important new developments within the system include two‐phase estimation as well as the estimation of variance for a number of imputation procedures. We briefly review the status of current estimation research on selected topics as well as the direction of future research.
Sampling and Estimation Issues for Annual and Sub‐annual Canadian Business Surveys
Authors: Hidiroglou Michael A. , Laniel Normand 
Published: May 2007

SummaryA typical Business Register (BR) is mainly based on administrative data files provided by organisations that produce them as a by‐product of their function. Such files do not necessarily yield a perfect Business Register. A good BR should have the following characteristics: (1) It should reflect the complex structures of businesses with multiple activities, in multiple locations or with multiple legal entities; (2) It should be free of duplication, extraneous or missing units; (3) It should be properly classified in terms of key stratification variables, including size, geography and industry; (4) It should be easily updateable to represent the “newer” business picture, and not lag too much behind it. In reality, not all these desirable features are fully satisfied, resulting in a universe that has missing units, inaccurate structures, as well as improper contact information, to name a few defects.These defects can be compensated by using sampling and estimation procedures. For example, coverage can be improved using multiple frame techniques, and the sample size can be increased to account for misclassification of units and deaths on the register. At the time of estimation, auxiliary information can be used in a variety of ways. It can be used to impute missing variables, to treat outliers, or to create synthetic variables obtained via modelling. Furthermore, time lags between the birth of units and the time that they are included on the register can be accounted for appropriately inflating the design‐based estimates.
Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics
Authors: Cleveland William S. 
Published: May 2007

SummaryAn action plan to enlarge the technical areas of statistics focuses on the data analyst. The plan sets out six technical areas of work for a university department, and advocates a specific allocation of resources devoted to research in each area and to courses in each area. The value of technical work is judged by the extent to which it benefits the data analyst, either directly or indirectly. The plan is also applicable to government research labs and corporate research organizations.
A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping
Authors: Waagepetersen Rasmus , Sorensen Daniel 
Published: May 2007

SummaryA tutorial derivation of the reversible jump Markov chain Monte Carlo (MCMC) algorithm is given. Various examples illustrate how reversible jump MCMC is a general framework for Metropolis‐Hastings algorithms where the proposal and the target distribution may have densities on spaces of varying dimension. It is finally discussed how reversible jump MCMC can be applied in genetics to compute the posterior distribution of the number, locations, effects, and genotypes of putative quantitative trait loci.
A Simulation Approach to Nonparametric Empirical Bayes Analysis
Authors: Dellaportas Petros , Karlis Dimitris 
Published: May 2007

SummaryWe deal with general mixture of hierarchical models of the formm(x) = føf(x |θ) g (θ)dθ, whereg(θ)andm(x)are called mixing and mixed or compound densities respectively, and θ is called the mixing parameter. The usual statistical application of these models emerges when we have dataxi, i = 1,…,nwith densitiesf(xi|θi)for given θi, and the θ1are independent with common densityg(θ). For a certain well known class of densitiesf(x |θ), we present a sample‐based approach to reconstructg(θ). We first provide theoretical results and then we use, in an empirical Bayes spirit, the first four moments of the data to estimate the first four moments ofg(θ). By using sampling techniques we proceed in a fully Bayesian fashion to obtain any posterior summaries of interest. Simulations which investigate the operating characteristics of our proposed methodology are presented. We illustrate our approach using data from mixed Poisson and mixed exponential densities.
Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns*
Authors: Mateu Jorge , Montes Francisco 
Published: May 2007

SummarySeveral authors have proposed stochastic and non‐stochastic approximations to the maximum likelihood estimate (MLE) for Gibbs point processes in modelling spatial point patterns with pairwise interactions. The approximations are necessary because of the difficulty of evaluating the normalizing constant. In this paper, we first provide a review of methods which yield crude approximations to the MLE. We also review methods based on Markov chain Monte Carlo techniques for which exact MLE has become feasible. We then present a comparative simulation study of the performance of such methods of estimation based on two simulation techniques, the Gibbs sampler and the Metropolis‐Hastings algorithm, carried out for the Strauss model.
Some Cracks in the Empire of Chance (Flaws in the Foundations of Reliability)
Authors: Singpurwalla Nozer D. 
Published: May 2007

SummaryMaking quantified statements about the uncertainty associated with the lifelength of an item is one of the most fundamental tasks of reliability assessment. Most practitioners routinely do this using one of the several available statistical techniques. The purpose of this paper is two‐fold. The first is to give the user an overview of the key tenets of two of the most commonly used parametric approaches. The second is to point out that these commonly used approaches involve strategies that are either ad hoc, or are in violation of some of the underlying tenets. A method that is devoid of logical flaws can be proposed, but this method is difficult to implement. The user must therefore resign to using that technique against which the fewest objections can be hurled.