bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of the thyroid status of Basenji dogs in Australia
Authors: Seavers A , Snow DH , Mason KV , Malik R 
Published: August 2010

Objective To determine the thyroid status of Basenji dogs in Australia.Methods Jugular or cephalic venipuncture blood samples were taken from 113 Basenji, comprising 47 males, 5 castrates, 48 entire and 13 spayed bitches, and sent on ice in plain and EDTA tubes to a single laboratory to determine haematocrit and serum concentrations of total thyroid hormone (thyroxine, TT4), thyroid‐stimulating hormone (TSH) and cholesterol. In a subgroup of 8 dogs with abnormal elevated TSH concentrations and subnormal TT4 concentrations, 5 were further examined by dynamic endocrine testing using recombinant human (rh) TSH (54 µg).Results Ages ranged from 1 to 14 years and weight range was 6.5 to 14.0 kg. TT4 concentrations (nmol/L) ranged from 2 to 27, with a median of 13 and a mean ± SD of 13.0 ± 5.7. Importantly, 85/113 (75%) of TT4 values were lower than the normal laboratory reference range (17–37). TSH concentrations (ng/mL) ranged from 0.05 to 5.37, with a median of 0.16 and a mean ± SD of 0.3 ± 0.6.Conclusions Basenji have a similar reference range for serum TSH, but a considerably lower reference range for TT4 (2–27 nmol/L) than most breeds and crossbreds, resembling the sight hounds in this respect. Given the difficulty of accurately measuring TT4 concentrations that are so low, concomitant serial TSH determinations are essential to properly asses thyroid function. Taken alone, TT4 determinations are only of use when the value is within the reference range, in which case a diagnosis of hypothyroidism is likely excluded.
Stochastic Optimization: a Review
Authors: Fouskakis Dimitris , Draper David 
Published: August 2010

SummaryWe review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via maximization of expected utility—of a variable selection problem in generalized linear models, which arises in the cost‐effective construction of a patient sickness‐at‐admission scale as part of an effort to measure quality of hospital care.
Bayesian Hypothesis Testing: a Reference Approach
Authors: Bernardo José M. , Rueda Raúl 
Published: August 2010

SummaryFor any probability modelM={p(x|θ, ω), θεΘ, ωεΩ} assumed to describe the probabilistic behaviour of dataxεX, it is argued that testing whether or not the available data arecompatiblewith the hypothesisH0={θ=θ0} is best considered as a formal decision problem on whether to use (a0), or not to use (a0), the simpler probability model (ornullmodel)M0={p(x|θ0, ω), ωεΩ}, where the loss differenceL(a0, θ, ω) –L(a0, θ, ω) is proportional to the amount of information δ(θ0, ω), which would be lost if the simplified modelM0were used as a proxy for the assumed modelM. For any prior distribution π(θ, ω), the appropriate normative solution is obtained by rejecting the null modelM0whenever the corresponding posterior expectation ∫∫δ(θ0, θ, ω)π(θ, ω|x)dθdω is sufficiently large.Specification of a subjective prior is always difficult, and often polemical, in scientific communication. Information theory may be used to specify a prior, thereferenceprior, which only depends on the assumed modelM, and mathematically describes a situation where no prior information is available about the quantity of interest. The reference posterior expectation,d(θ0,x) =∫δπ(δ|x)dδ, of the amount of information δ(θ0, θ, ω) which could be lost if the null model were used, provides an attractive nonnegative test function, theintrinsic statistic, which is invariant under reparametrization.The intrinsic statisticd(θ0,x) is measured in units of information, and it is easily calibrated (for any sample size and any dimensionality) in terms of some average log‐likelihood ratios. The corresponding Bayes decision rule, theBayesian reference criterion (BRC), indicates that the null modelM0should only be rejected if the posterior expected loss of information from using the simplified modelM0is too large or, equivalently, if the associated expected average log‐likelihood ratio is large enough.The BRC criterion provides a general reference Bayesian solution to hypothesis testing which does not assume a probability mass concentrated onM0and, hence, it is immune to Lindley's paradox. The theory is illustrated within the context of multivariate normal data, where it is shown to avoid Rao's paradox on the inconsistency between univariate and multivariate frequentist hypothesis testing.
Nonparametric Varying‐Coefficient Models for the Analysis of Longitudinal Data
Authors: Wu Colin O. , Yu Kai F. 
Published: August 2010

SummaryLongitudinal methods have been widely used in biomedicine and epidemiology to study the patterns of time‐varying variables, such as disease progression or trends of health status. Data sets of longitudinal studies usually involve repeatedly measured outcomes and covariates on a set of randomly chosen subjects over time. An important goal of statistical analyses is to evaluate the effects of the covariates, which may or may not depend on time, on the outcomes of interest. Because fully parametric models may be subject to model misspecification and completely unstructured nonparametric models may suffer from the drawbacks of “curse of dimensionality”, the varying‐coefficient models are a class of structural nonparametric models which are particularly useful in longitudinal analyses. In this article, we present several important nonparametric estimation and inference methods for this class of models, demonstrate the advantages, limitations and practical implementations of these methods in different longitudinal settings, and discuss some potential directions of further research in this area. Applications of these methods are illustrated through two epidemiological examples.
Graphical Diagnostics for Modeling Unstructured Covariance Matrices
Authors: Pourahmadi Mohsen 
Published: August 2010

SummaryPrudent statistical analysis of correlated data requires accounting for the correlation among the measurements. Specifying a form for the covariance matrix of the data could reduce the high number of parameters of the covariance and increase efficiency of the inferences about the regression parameters. Motivated by the success of ordinary, partial and inverse correlograms in identifying parsimonious models for stationary time series, we introduce generalizations of these plots for nonstationary data. Their roles in detecting heterogeneity and correlation of the data and identifying parsimonious models for the covariance matrix are illuminated using a longitudinal dataset. Decomposition of a covariance matrix into “variance” and “dependence” components provides the necessary ingredients for the proposed graphs. This amounts to replacing a 3‐D correlation plot by a pair of 2‐D plots, providing complementary information about dependence and heterogeneity. Models identified and fitted using the variance‐correlation decomposition of a covariance matrix are not guaranteed to be positive definite, but those using the modified Cholesky decomposition are. Limitations of our graphical diagnostics for general multivariate data where the measurements are not (time‐) ordered are discussed.
On Choosing and Bounding Probability Metrics
Authors: Gibbs Alison L. , Su Francis Edward 
Published: August 2010

SummaryWhen studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.
Bayesian International Evidence on Heavy Tails, Non‐Stationarity and Asymmetry over the Business Cycle
Authors: Tsionas Efthymios G. 
Published: August 2010

SummaryAlthough leptokurtosis is fairly common in macroeconomic time series, agreement over what non‐normal distributions are plausible, is rare. The paper proposes a linear model that allows for trend versus difference stationarity and asymmetric behavior over the business cycle along with several distributional alternatives for the disturbance terms. It proposes computationally feasible Markov Chain Monte Carlo methods to perform Bayesian computations, applies the model to industrial production data of seven industrialized countries, and relies on prior predictive densities to compare models with Student‐t, symmetric stable, EGARCH, exponential power family and finite‐mixture‐of‐normals errors. The relationship between unit root inference, asymmetry and leptokurtosis is examined in detail using the exact, finite‐sample posteriors corresponding to the different models.
Environmental Statistics—A Personal View
Authors: Guttorp Peter 
Published: August 2010

SummaryThe field of environmental statistics is one of rapid growth at the moment. Environmental decisionmaking is prevalent in much of the world, and politicians and other decision makers are requesting new tools for understanding the state of the environment. In this paper, three case studies involving water pollution, air pollution, and climate change assessment are presented, together with brief descriptions some other areas of environmental statistics. A discussion of future directions of the field concludes the paper.
Statistical Assessment of Numerical Models*
Authors: Fuentes Montserrat , Guttorp Peter , Challenor Peter 
Published: August 2010

SummaryEvaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. The high risk of getting the wrong control strategy has costly economic and social consequences. The objective comparison of modeled concentrations with observed field data is one approach to assessment of model performance. For dry deposition fluxes and concentrations of air pollutants there is a very limited supply of evaluation data sets. We develop a formal method for evaluation of the performance of numerical models, which can be implemented even when the field measurements are very sparse. This approach is applied to a current U.S. Environmental Protection Agency air quality model. In other cases, exemplified by an ozone study from the California Central Valley, the observed field is relatively data rich, and more or less standard geostatistical tools can be used to compare model to data. Yet another situation is when the cost of model runs is prohibitive, and a statistical approach to approximating the model output is needed. We describe two ways of obtaining such approximations.A common technical issue in the assessment of environmental numerical models is the need for tools to estimate nonstationary spatial covariance structures. We describe in detail two such approaches.
Reflections on Fourteen Cryptic Issues Concerning the Nature of Statistical Inference*
Authors: Kardaun O. J. W. F. , Salomè D. , Schaafsma W. , Steerneman A. G. M. , Willems J. C. , Cox D.R. 
Published: August 2010

SummaryThe present paper provides the original formulation and a joint response of a group of statistically trained scientists to fourteen cryptic issues for discussion, which were handed out to the public by Professor Dr. D.R. Cox after his Bernoulli Lecture 1997 at Groningen University.
Some Aspects Of Neutral To Right Priors
Authors: Dey Jyotirmoy , Erickson R.V. , Ramamoorthi R.V. 
Published: August 2010

SummaryNeutral to right priors are generalizations of Dirichlet process priors that fit in well with right‐censored data. These priors are naturally induced by increasing processes with independent increments which, in turn, may be viewed as priors for the cumulative hazard function. This connection together with the Lévy representation of independent increment processes provides a convenient means of studying properties of neutral to right priors.This article is a review of the theoretical aspects of neutral to right priors and provides a number of new results on their structural properties. Notable among the new results are characterizations of neutral to right priors in terms of the posterior and the cumulative hazard function. We also show that neutral to right priors are of the following nature: Consistency of Bayes’ estimates implies consistency of the posterior, and posterior‐consistency for complete observations automatically yields posterior‐consistency for right‐censored data.
Multiple Imputation: Theory and Method
Authors: Zhang Paul 
Published: August 2010

SummaryIn this review paper, we discuss the theoretical background of multiple imputation, describe how to build an imputation model and how to create proper imputations. We also present the rules for making repeated imputation inferences. Three widely used multiple imputation methods, the propensity score method, the predictive model method and the Markov chain Monte Carlo (MCMC) method, are presented and discussed.
Proper and Improper Multiple Imputation
Authors: Nielsen Søren Feodor 
Published: August 2010

Summary Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper. So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximately Bayesian multiple imputations has been shown to lead to proper imputations in some settings. In this paper, we shall see that Bayesian multiple imputation does not generally lead to proper multiple imputations. Furthermore, it will be argued that for general statistical use, Bayesian multiple imputation is inefficient even when it is proper.
Dimension Reduction with Linear Discriminant Functions Based on an Odds Ratio Parameterization
Authors: van der Linde Angelika 
Published: August 2010

SummaryThe association of two random elements with positive joint probability density function is given by an odds ratio function. The covariance is an adequate description only in the case of two jointly Gaussian variables. The impact of the association structure on the set‐up and solution of problems of linear discrimination is investigated, and the results are related to standard techniques of multivariate analysis, particularly to canonical correlation analysis, analysis of contingency tables, discriminant analysis and multidimensional scaling.RésuméL'association entre deux éléments aléatoires ayant une densité conjointe positive est donnée par l'odds ratio fonction. La covariance n'est une description adéquate que dans le cas gaussien. L'impact de ce résultat en termes de réduction de la dimension et en terme de discrimination est examinée et les résultats sont rapportés aux techniques traditionelles de l'analyse discriminante et du scaling multidimensional.
Mortality Forecasting and Trend Shifts: an Application of the Lee–Carter Model to Swedish Mortality Data*
Authors: Lundström Hans , Qvist Jan 
Published: August 2010

Summary In this paper we examine how the Lee–Carter model fares with Swedish data for the period 1901–2001 and for segments of this period. We have choosen to censor ages less than age 40 as those ages only are of marginal interest to the forecast. At age 40 some 98 to 99 percent of the birth cohorts are survivors. In the study we only consider the unweighted K1 estimates. The Lee–Carter model provides very good fits to the data. When splitting up the base period there seems to be an interaction beween the age and time components of the model. In order to deal with the different phases of falling mortality for males and females possibly one should choose the past 25 years as a base in the model. Selecting the base period is however a judgmental issue depending on the main focus of the forecast. Is it long‐term, short‐term or, as in Sweden, a combination of both.?
Conditional Probabilistic Population Forecasting
Authors: Sanderson Warren C. , Scherbov Sergei , O'Neill Brian C. , Lutz Wolfgang 
Published: August 2010

Summary Since policy‐makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy‐makers because it allows them to answer “what if” type questions properly when outcomes are uncertain. The second is a new category that we call “future jump‐off date forecasts”. Future jump‐off date forecasts are valuable because they show policy‐makers the likelihood that crucial features of today's forecasts will also be present in forecasts made in the future.
Conditional Probabilistic Population Projections: An Application to Climate Change
Authors: O'Neill Brian C. 
Published: August 2010

SummaryFuture changes in population size, composition, and spatial distribution are key factors in the analysis of climate change, and their future evolution is highly uncertain. In climate change analyses, population uncertainty has traditionally been accounted for by using alternative scenarios spanning a range of outcomes. This paper illustrates how conditional probabilistic projections offer a means of combining probabilistic approaches with the scenario‐based approach typically employed in the development of greenhouse gas emissions projections. The illustration combines a set of emissions scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) with existing probabilistic population projections from IIASA. Results demonstrate that conditional probabilistic projections have the potential to account more fully for uncertainty in emissions within conditional storylines about future development patterns, to provide a context for judging the consistency of individual scenarios with a given storyline, and to provide insight into relative likelihoods across storylines, at least from a demographic perspective. They may also serve as a step toward more comprehensive quantification of uncertainty in emissions projections.
A Comparative Simulation Study of Wavelet Shrinkage Estimators for Poisson Counts
Authors: Besbeas Panagiotis , De Feis Italia , Sapatinas Theofanis 
Published: August 2010

SummaryUsing computer simulations, the finite sample performance of a number of classical and Bayesian wavelet shrinkage estimators for Poisson counts is examined. For the purpose of comparison, a variety of intensity functions, background intensity levels, sample sizes, primary resolution levels, wavelet filters and performance criteria are employed. A demonstration is given of the use of some of the estimators to analyse a data set arising in high‐energy astrophysics. Following the philosophy of reproducible research, the Matlabprograms and real‐life data example used in this study are made freely available.
A Foundational Justification for a Weighted Likelihood Approach to Inference
Authors: Bowater Russell J. 
Published: August 2010

Summary Two types of probability are discussed, one of which is additive whilst the other is non‐additive. Popular theories that attempt to justify the importance of the additivity of probability are then critically reviewed. By making assumptions the two types of probability put forward are utilised to justify a method of inference which involves betting preferences being revised in light of the data. This method of inference can be viewed as a justification for a weighted likelihood approach to inference where the plausibility of different values of a parameter θ based on the data X̄ is measured by the quantity q(θ) =l(X̄, θ)w(θ), where l(X̄, θ) is the likelihood function and w(θ) is a weight function. Even though, unlike Bayesian inference, the method has the disadvantageous property that the measure q(θ) is generally non‐additive, it is argued that the method has other properties which may be considered very desirable and which have the potential to imply that when everything is taken into account, the method is a serious alternative to the Bayesian approach in many situations. The methodology that is developed is applied to both a toy example and a real example.
Statistical Inference under Symmetry
Authors: Helland Inge S. 
Published: August 2010

SummaryWe explore the consequences of adjoining a symmetry group to a statistical model. Group actions are first induced on the sample space, and then on the parameter space. It is argued that the right invariant measure induced by the group on the parameter space is a natural non‐informative prior for the parameters of the model. The permissible sub‐parameters are introduced, i.e., the subparameters upon which group actions can be defined. Equivariant estimators are similarly defined. Orbits of the group are defined on the sample space and on the parameter space; in particular the group action is called transitive when there is only one orbit. Credibility sets and confidence sets are shown (under right invariant prior and assuming transitivity on the parameter space) to be equal when defined by permissible sub‐parameters and constructed from equivariant estimators. The effect of different choices of transformation group is illustrated by examples, and properties of the orbits on the sample space and on the parameter space are discussed. It is argued that model reduction should be constrained to one or several orbits of the group. Using this and other natural criteria and concepts, among them concepts related to design of experiments under symmetry, leads to links towards chemometrical prediction methods and towards the foundation of quantum theory.
Causality and Causal Models: A Conceptual Perspective*
Authors: Frosini Benito V. 
Published: August 2010

SummaryThis paper aims at displaying a synthetic view of the historical development and the current research concerning causal relationships, starting from the Aristotelian doctrine of causes, following with the main philosophical streams until the middle of the twentieth century, and commenting on the present intensive research work in the statistical domain. The philosophical survey dwells upon various concepts of cause, and some attempts towards picking out spurious causes. Concerning statistical modelling, factorial models and directed acyclic graphs are examined and compared. Special attention is devoted to randomization and pseudo‐randomization (for observational studies) in view of avoiding the effect of possible confounders. An outline of the most common problems and pitfalls, encountered in modelling empirical data, closes the paper, with a warning to be very cautious in modelling and inferring conditional independence between variables.