bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measuring Knowledge Development and Developing Official Statistics for the Information Age
Authors: Ramachandran R. 
Published: August 2010

Summary Measuring knowledge development is a new statistical activity that warrants urgent attention in the light of the current Internet explosion. The Internet creates virtual networks by connecting information nodes, knowledge nexus, people and institutions. The Internet has resulted in an unprecedented proliferation of Information, Communication, Knowledge and Entertainment (ICKE), which has in turn brought about structural changes in all aspects of social, economic and political governance. For public policy formulators, including the statistical community, it is imperative that the knowledge development aspect of ICKE be measured. Being abstract, knowledge is difficult to quantify. However, the manifestations of attributes and variables of any knowledge development activity are measurable. The paper outlines a conceptual framework for achieving this. This proposed framework adopts a socio‐technological approach, premised on contemporary information and knowledge development as an integral of the people and technology dimensions. To illustrate the workability of the proposed model, the paper identifies some parameters and variables in the current statistical system, and highlights some new data generated via the Internet Subscriber Study and ICT Exposition Visitor Study. All illustrations refer to Malaysian data. Finally, the paper outlines ‘way forward’ initiatives for establishing a full‐fledged set of information and knowledge development indicators.
Statistical Regularity and Free Will: L.A.J. Quetelet and P.A. Nekrasov
Authors: Seneta Eugene 
Published: August 2010

SummaryIn the 19th century, causes of empirically observed stability of averages in settings relating to human behaviour were a topic of intense discussion in western Europe. This followed an extensive study of empirical stability by the founder of modern statistics (and of the International Statistical Institute) L.A.J. Quetelet, published in 1835, in what he called “Social Physics”. The eminent mathematician of strong probabilistic and philosophical inclination and Russian Orthodox religious belief, P.A. Nekrasov, took up and modified Quetelet's Social Physics in 1902, with (social) independence seen as prime cause of statistical regularity. Our paper focuses on the role free will plays in the statistical writings of Quetelet and of Nekrasov. The work of the latter has remained little known in general, mainly for politico‐ideological reasons.
Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling
Authors: Kuhnert Petra M. , Mengersen Kerrie , Tesar Peter 
Published: August 2010

SummaryThis paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well‐known and long standing traditional idea of ‘good practice in statistics’ by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions.The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non‐parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non‐parametric and Bayesian approaches.This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.
A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing*
Authors: Gelman Andrew 
Published: August 2010

SummaryExploratory data analysis (EDA) and Bayesian inference (or, more generally, complex statistical modeling)—which are generally considered as unrelated statistical paradigms—can be particularly effective in combination. In this paper, we present a Bayesian framework for EDA based on posterior predictive checks. We explain how posterior predictive simulations can be used to create reference distributions for EDA graphs, and how this approach resolves some theoretical problems in Bayesian data analysis. We show how the generalization of Bayesian inference to include replicated datayrepand replicated parametersθrepfollows a long tradition of generalizations in Bayesian theory.On the theoretical level, we present a predictive Bayesian formulation of goodness‐of‐fit testing, distinguishing betweenp‐values (posterior probabilities that specified antisymmetric discrepancy measures will exceed 0) andu‐values (data summaries with uniform sampling distributions). We explain thatp‐values, unlikeu‐values, are Bayesian probability statements in that they condition on observed data.Having reviewed the general theoretical framework, we discuss the implications for statistical graphics and exploratory data analysis, with the goal being to unify exploratory data analysis with more formal statistical methods based on probability models. We interpret various graphical displays as posterior predictive checks and discuss how Bayesian inference can be used to determine reference distributions.The goal of this work is not to downgrade descriptive statistics, or to suggest they be replaced by Bayesian modeling, but rather to suggest how exploratory data analysis fits into the probability‐modeling paradigm.We conclude with a discussion of the implications for practical Bayesian inference. In particular, we anticipate that Bayesian software can be generalized to draw simulations of replicated data and parameters from their posterior predictive distribution, and these can in turn be used to calibrate EDA graphs.
Statistical Surveillance. Optimality and Methods
Authors: Frisén Marianne 
Published: August 2010

SummaryDifferent criteria of optimality are used in different subcultures of statistical surveillance. One aim with this review is to bridge the gap between the different areas. The shortcomings of some criteria of optimality are demonstrated by their implications. Some commonly used methods are examined in detail, with respect to optimality. The examination is made for a standard situation in order to focus on the inferential principles. A uniform presentation of methods, by expressions of likelihood ratios, facilitates the comparisons between methods. The correspondences between criteria of optimality and methods are examined. The situations and parameter values for which some commonly used methods have optimality properties are thus determined. A linear approximation of the full likelihood ratio method, which satisfies several criteria of optimality, is presented. This linear approximation is used to examine when linear methods are approximately optimal. Methods for complicated situations are reviewed with respect to optimality and robustness.
Sentencing Illicit Drug Traffickers: How do the Courts Handle Random Sampling Issues?
Authors: Izenman Alan J. 
Published: August 2010

SummaryWhile many European justice systems distinguish between possession of and trafficking in illicit drugs, sentencing in drug cases in those countries tends not to depend (at least formally) upon the quantity of drugs seized from a defendant, but rather on the circumstances in which the defendant was found with drugs. Courts in the United States, on the other hand, penalize those convicted of drug crimes through an elaborate system of sentencing rules and guidelines. These sentences depend only upon the amount of drugs (possibly adjusted for circumstances) and the defendant's criminal history. Because of the enormous amount of work needed to determine drug type and quantity in each case, sampling the evidence and estimates of drug quantity have been accepted in most courts in the United States. During the last two years, several major developments in U.S. federal sentencing have taken place. It all started in June 2000 with the Supreme Court case Apprendi v. New Jersey, in which Justice O'Connor, in dissent, termed a “watershed in constitutional law”. Prior toApprendi, a judge would sentence a convicted drug trafficker to imprisonment using the preponderance standard of proof on the quantities of drugs seized. The affect ofApprendiis to make more juries decide the quantity of drugs by the reasonable doubt standard. AlthoughApprendihad nothing to do with illicit drugs, the implication now is that all federal sentencing protocols are under revision. SinceApprendiwas decided, huge numbers of cases involving convicted drug traffickers have relied upon its reasoning on appeal, with the result that a ruling from the Supreme Court on further clarification of this issue is expected. This article discusses the impact so far ofApprendion statistical issues involved in estimating total drug quantity and considers possible future directions under the changing protocols.
Discussion: Efficiency and Self‐efficiency With Multiple Imputation Inference
Authors: Meng Xiao‐Li , Romero Martin 
Published: August 2010

Summary By closely examining the examples provided in Nielsen (2003), this paper further explores the relationship between self‐efficiency (Meng, 1994) and the validity of Rubin's multiple imputation (RMI) variance combining rule. The RMI variance combining rule is based on the common assumption/intuition that the efficiency of our estimators decreases when we have less data. However, there are estimation procedures that will do the opposite, that is, they can produce more efficient estimators with less data. Self‐efficiency is a theoretical formulation for excluding such procedures. When a user, typically unaware of the hidden self‐inefficiency of his choice, adopts a self‐inefficient complete‐data estimation procedure to conduct an RMI inference, the theoretical validity of his inference becomes a complex issue, as we demonstrate. We also propose a diagnostic tool for assessing potential self‐inefficiency and the bias in the RMI variance estimator, at the outset of RMI inference, by constructing a convenient proxy to the RMI point estimator.
Random Scenario Forecasts Versus Stochastic Forecasts
Authors: Tuljapurkar Shripad , Lee Ronald D. , Li Qi 
Published: August 2010

Summary Probabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts. Another (we call it RS) began as a kind of randomized scenario: we consider its simplest variant, in which expert opinion is used to make probability distributions for terminal vital rates, and smooth trajectories are followed over time. We use analysis and examples to show several key differences between these methods: serial correlations in the forecast are much smaller in LT; the variance in LT models of vital rates (especially fertility) is much higher than in RS models that are based on official expert scenarios; trajectories in LT are much more irregular than in RS; probability intervals in LT tend to widen faster over forecast time. Newer versions of RS have been developed that reduce or eliminate some of these differences.
Corbelled Domes in Two and Three Dimensions: The Treasury of Atreus
Authors: Lazar Nicole A. , Kadane Joseph B. , Chen Fang , Cavanagh William G. , Litton Clifford D. 
Published: August 2010

Summary Before the development of the true dome, many ancient cultures used the technique of corbelling to roof spaces. Recently, a series of related statistical models have been proposed in the literature for explaining how corbelled domes might have been constructed. The most sophisticated of these models is based on a piecewise linear structure, with an unknown number of changepoints, to guide the building process. This model is analyzed by the reversible jump Markov Chain Monte Carlo (MCMC) technique. All models considered to date have been two‐dimensional, that is, they have taken a single cross section through the dome; even when more extensive data, in the form of measurements on multiple slices through the dome, have been available, these have been averaged together for the purposes of analysis. In this paper, we extend the two‐dimensional analysis to a three‐dimensional analysis, that takes full advantage of the data collected by the archaeologists and of the rotational symmetries inherent in the structure.We also explore ways of graphically presenting the results from a complex, reversible jump MCMC implementation, in order to check convergence, good mixing, and appropriate exploration of the (high dimensional and varying dimension) parameter space. The model and the graphical techniques are demonstrated on the Treasury of Atreus in Mycenae, Greece, one of the finest extant examples of the corbelling method.
Simple Correspondence Analysis: A Bibliographic Review
Authors: Beh Eric J. 
Published: August 2010

Summary Over the past few decades correspondence analysis has gained an international reputation as a powerful statistical tool for the graphical analysis of contingency tables. This popularity stems from its development and application in many European countries, especially France, and its use has spread to English speaking nations such as the United States and the United Kingdom. Its growing popularity amongst statistical practitioners, and more recently those disciplines where the role of statistics is less dominant, demonstrates the importance of the continuing research and development of the methodology. The aim of this paper is to highlight the theoretical, practical and computational issues of simple correspondence analysis and discuss its relationship with recent advances that can be used to graphically display the association in two‐way categorical data.
A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models
Authors: Harris Mark N. , Mátyás László 
Published: August 2010

Summary It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek–Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro‐datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.
Critique of p‐Values
Authors: Thompson Bill 
Published: August 2010

SummaryThis paper generalizes the notion of p‐value to obtain a system for assessing evidence in favor of an hypothesis. It is not quite a quantification in that evidence is a pair of numbers (the p‐value and the p‐value with null and alternative interchanged) with evidence for the alternative being claimed when the first number is small and the second is at least moderately large. Traditional significance tests present p‐values as a measure of evidence against a theory. This usage is rarely called for since scientists usually wish to accept theories (for the time being) not just not reject them; they are more interested in evidence for a theory. P‐values are not just good or bad for this purpose; their efficacy depends on specifics. We find that a single p‐value does not measure evidence for a simple hypothesis relative to a simple alternative, but consideration of both p‐values leads to a satisfactory theory. This consideration does not, in general, extend to composite hypotheses since there, best evidence calls for optimization of a bivariate objective function. But in some cases, notably one sided tests for the exponential family, the optimization can be solved, and a single p‐value does provide an appealing measure of best evidence for a hypothesis. One possible extension of this theory is proposed and illustrated with a practical safety analysis problem involving the difference of two random variables.
Smoothing Observational Data: A Philosophy and Implementation for the Health Sciences
Authors: Greenland Sander 
Published: August 2010

SummaryStandard statistical methods (such as regression analysis) presume the data are generated by an identifiable random process, and attempt to model that process in a parsimonious fashion. In contrast, observational data in the health sciences are generated by complex, nonidentified, and largely nonrandom mechanisms, and are analyzed to form inferences on latent structures. Despite this gap between the methods and reality, most observational data analysis comprises application of standard methods, followed by narrative discussion of the problems of entailed by doing so. Alternative approaches employ latent‐structure models that include components for nonidentified mechanisms. Standard methods can still be useful, however, provided their modeling philosophy is modified to encourage preservation of structure, rather than achieving parsimonious description. With this modification they can be viewed as smoothing or filtering methods for separating noise from signal before the task of latent‐structure modeling begins. I here give a detailed justification of this view, and a hierarchical‐modeling implementation that can be carried out with popular software. Concepts are illustrated in the smoothing of a contingency table from an analysis of magnetic fields and childhood leukemia.
Skewed Normal Variance‐Mean Models for Asset Pricing and the Method of Moments
Authors: Tjetjep Annelies , Seneta Eugene 
Published: August 2010

Summary Financial returns (log‐increments) data, Yt, t= 1,2,…, are treated as a stationary process, with the common distribution at each time point being not necessarily symmetric. We consider as possible models for the common distribution four instances of the General Normal Variance‐Mean Model (GNVM), which is described by Y|V∼N(a(b + V), c2V +d2) where V is a nonnegative random variable and a, b, c and d are constants. When V is Gamma distributed and d= 0, Y has the skewed Variance‐Gamma distribution (VG). When V follows a Half Normal distribution and c= 0, Y has the well‐known Skew Normal (SN) distribution. We also consider two cases where V is Exponentially distributed. Bounds for skewness and kurtosis in each case are found in terms of the moments of the V. These are useful in determining whether the Method of Moments for a given model is feasible. The problem of overdetermination of parameters via estimating equations is examined. 5 data sets of actual returns data, chosen because of their earlier occurrence in the literature, are analysed using each of the 4 models.
Comparison of Benchmarking Methods with and without a Survey Error Model
Authors: Chen Zhao‐Guo , Wu Ka Ho 
Published: August 2010

SummaryFor a target socio‐economic variable, two sources of data with different precisions and collecting frequencies may be available. Typically, the less frequent data (e.g., annual report or census) are more reliable and are considered as benchmarks. The process of using them to adjust the more frequent and less reliable data (e.g., repeated monthly surveys) is called benchmarking.In this paper, we show the relationship among three types of benchmarking methods in the literature, namely the Denton (original and modified), the regression, and the signal‐extraction methods. A new method called “quasi‐linear regression” is proposed under the multiplicative assumption. The numerical Denton method is currently widely used. The aim of this paper is to promote the other two methods which are statistically model‐based; the model for the survey error is assumed to be known. Assuming the survey‐error series follows an autoregressive model of order 1, by simulation, we investigate the impact of misspecification of the model on the benchmarking prediction according to the criterion of minimizing the root‐mean‐squared error of prediction. It is concluded that both statistical methods have great advantages over the Denton method and they are robust to misspecification of the survey‐error model. The problem of how to obtain a survey‐error model is also mentioned.
Improving Statistics on International Migration in Asia
Authors: Hugo Graeme 
Published: August 2010

SummaryInternational migration has reached unprecedented scale, diversity and political, economic, social and demographic significance in Asia over the last decade. Despite this data collection of migrant stocks and flows remains very limited in most Asian countries. Accordingly, policy making on migration in the region lacks an evidence base and is influenced by interest groups, anecdotal evidence and prejudice. This paper argues that the heightened security consciousness since 911 together with the development of efficient computer based collection and analysis of migration data systems has created a propitious environment for bringing about a parametric improvement in data collection on international migration in Asia. A number of suggestions are made in this regard, especially the inclusion of relevant international migration questions in the 2010 round of population censuses.
Towards an Integrated Statistical System at Statistics Netherlands*
Authors: Heerschap Nico , Willenborg Leon 
Published: August 2010

SummaryChanges in circumstances put pressure on Statistics Netherlands (SN) to redesign the way its statistics are produced. Key developments are: the changing needs of data‐users, growing competition, pressure to reduce the survey burden on enterprises, emerging new technologies and methodologies and, first and foremost, the need for more efficiency because of budget cuts.This paper describes how SN, and especially its business statistics, can adapt to these new circumstances. We envisage an optimum situation as one with a single standardised production line for all statistics and a central data repository at its core. This single production line is supported by generic and standardised tools, metadata and workflow management.However, it is clear that such an optimum situation cannot be realised in just a few years. It should be seen as the point on the horizon. Therefore, we also describe the first transformation steps from the product‐based stovepipe‐oriented statistical process of the past to a more integrated process of the future.A similar modernisation process exists in the area of social statistics. In the near future both systems of business and social statistics are expected to connect at pivotal points and eventually converge on one overall business architecture for SN. Discussions about such an overall business architecture for SN have already been started and the first core projects have been set up.