bluuebunny/crossref_metadata_2025_split huggingface dataset:
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
Authors: Richardson Sylvia. , Green Peter J. 
Published: March 2003

Summary
New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.
A Bayesian Restoration of an Ion Channel Signal
Authors: Hodgson M. E. A. 
Published: March 2003

Summary
We present a Bayesian method of ion channel analysis and apply it to a simulated data set. An alternating renewal process prior is assigned to the signal, and an autoregressive process is fitted to the noise. After choosing model hyperconstants to yield ‘uninformative’ priors on the parameters, the joint posterior distribution is computed by using the reversible jump Markov chain Monte Carlo method. A novel form of simulated tempering is used to improve the mixing of the original sampler.
Statistical Applications of the Multivariate Skew Normal Distribution
Authors: Azzalini A. , Capitanio A. 
Published: March 2003

Summary
Azzalini and Dalla Valle have recently discussed the multivariate skew normal distribution which extends the class of normal distributions by the addition of a shape parameter. The first part of the present paper examines further probabilistic properties of the distribution, with special emphasis on aspects of statistical relevance. Inferential and other statistical issues are discussed in the following part, with applications to some multivariate statistics problems, illustrated by numerical examples. Finally, a further extension is described which introduces a skewing factor of an elliptical density.
Asymptotic Separability in Sensitivity Analysis
Authors: Gastwirth Joseph L. , Krieger Abba M. , Rosenbaum Paul R. 
Published: March 2003

Summary
In an observational study in which each treated subject is matched to several untreated controls by using observed pretreatment covariates, a sensitivity analysis asks how hidden biases due to unobserved covariates might alter the conclusions. The bounds required for a sensitivity analysis are the solution to an optimization problem. In general, this optimization problem is not separable, in the sense that one cannot find the needed optimum by performing a separate optimization in each matched set and combining the results. We show, however, that this optimization problem is asymptotically separable, so that when there are many matched sets a separate optimization may be performed in each matched set and the results combined to yield the correct optimum with negligible error. This is true when the Wilcoxon rank sum test or the Hodges-Lehmann aligned rank test is applied in matching with multiple controls. Numerical calculations show that the asymptotic approximation performs well with as few as 10 matched sets. In the case of the rank sum test, a table is given containing the separable solution. With this table, only simple arithmetic is required to conduct the sensitivity analysis. The method also supplies estimates, such as the Hodges-Lehmann estimate, and confidence intervals associated with rank tests. The method is illustrated in a study of dropping out of US high schools and the effects on cognitive test scores.
Partial Least Squares Estimator for Single-Index Models
Authors: Naik Prasad , Tsai Chih-Ling 
Published: March 2003

Summary
The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction. We study the performance of PLS in estimating single-index models, especially when the predictor variables exhibit high collinearity. We show that PLS estimates are consistent up to a constant of proportionality. We present three simulation studies that compare the performance of PLS in estimating single-index models with that of sliced inverse regression (SIR). In the first two studies, we find that PLS performs better than SIR when collinearity exists. In the third study, we learn that PLS performs well even when there are multiple dependent variables, the link function is non-linear and the shape of the functional form is not known.
Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
Authors: Barndorff-Nielsen Ole E. , Shephard Neil 
Published: March 2003

Summary
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.
Nonparametric Maximum Likelihood Estimation for Shifted Curves
Authors: Rønn Birgitte B. 
Published: March 2003

Summary
The analysis of a sample of curves can be done by self-modelling regression methods. Within this framework we follow the ideas of nonparametric maximum likelihood estimation known from event history analysis and the counting process set-up. We derive an infinite dimensional score equation and from there we suggest an algorithm to estimate the shape function for a simple shape invariant model. The nonparametric maximum likelihood estimator that we find turns out to be a Nadaraya–Watson-like estimator, but unlike in the usual kernel smoothing situation we do not need to select a bandwidth or even a kernel function, since the score equation automatically selects the shape and the smoothing parameter for the estimation. We apply the method to a sample of electrophoretic spectra to illustrate how it works.
Estimating the Structural Dimension of Regressions Via Parametric Inverse Regression
Authors: Bura Efstathia , Cook R. Dennis 
Published: March 2003

Summary
A new estimation method for the dimension of a regression at the outset of an analysis is proposed. A linear subspace spanned by projections of the regressor vector X, which contains part or all of the modelling information for the regression of a vector Y on X, and its dimension are estimated via the means of parametric inverse regression. Smooth parametric curves are fitted to the p inverse regressions via a multivariate linear model. No restrictions are placed on the distribution of the regressors. The estimate of the dimension of the regression is based on optimal estimation procedures. A simulation study shows the method to be more powerful than sliced inverse regression in some situations.
Effects of Model Misspecification on Tests of No Randomized Treatment Effect Arising From Cox’s Proportional Hazards Model
Authors: DiRienzo A. G. , Lagakos S. W. 
Published: March 2003

Summary
We examine the asymptotic and small sample properties of model-based and robust tests of the null hypothesis of no randomized treatment effect based on the partial likelihood arising from an arbitrarily misspecified Cox proportional hazards model. When the distribution of the censoring variable is either conditionally independent of the treatment group given covariates or conditionally independent of covariates given the treatment group, the numerators of the partial likelihood treatment score and Wald tests have asymptotic mean equal to 0 under the null hypothesis, regardless of whether or how the Cox model is misspecified. We show that the model-based variance estimators used in the calculation of the model-based tests are not, in general, consistent under model misspecification, yet using analytic considerations and simulations we show that their true sizes can be as close to the nominal value as tests calculated with robust variance estimators. As a special case, we show that the model-based log-rank test is asymptotically valid. When the Cox model is misspecified and the distribution of censoring depends on both treatment group and covariates, the asymptotic distributions of the resulting partial likelihood treatment score statistic and maximum partial likelihood estimator do not, in general, have a zero mean under the null hypothesis. Here neither the fully model-based tests, including the log-rank test, nor the robust tests will be asymptotically valid, and we show through simulations that the distortion to test size can be substantial.
Spatiotemporal Prediction for Log-Gaussian Cox Processes
Authors: Brix Anders , Diggle Peter J. 
Published: March 2003

Summary
Space–time point pattern data have become more widely available as a result of technological developments in areas such as geographic information systems. We describe a flexible class of space–time point processes. Our models are Cox processes whose stochastic intensity is a space–time Ornstein–Uhlenbeck process. We develop moment-based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and illustrate the performance of our methods on a synthetic data set.
Relative Efficiencies of Kernel and Local Likelihood Density Estimators
Authors: Hall Peter , Tao Terence 
Published: March 2003

SummaryLocal likelihood methods enjoy advantageous properties, such as good performance in the presence of edge effects, that are rarely found in other approaches to nonparametric density estimation. However, as we argue in this paper, standard kernel methods can have distinct advantages when edge effects are not present. We show that, whereas the integrated variances of the two methods are virtually identical, the integrated squared bias of a conventional kernel estimator is less than that of a local log-linear estimator by as much as a factor of 4. Moreover, the greatest bias improvements offered by kernel methods occur when they are needed most—i.e. when the effect of bias is particularly high. Similar comparisons can also be made when high degree local log-polynomial fits are assessed against high order kernel methods. For example, although (as is well known) high degree local polynomial fits offer potentially infinite efficiency gains relative to their kernel competitors, the converse is also true. Indeed, the asymptotic value of the integrated squared bias of a local log-quadratic estimator can exceed any given constant multiple of that for the competing kernel method. In all cases the densities that suffer problems in the context of local log-likelihood methods can be chosen to be symmetric, either unimodal or bimodal, either infinitely or compactly supported, and to have arbitrarily many derivatives as functions on the real line. They are not pathological. However, our results reveal quantitative differences between global performances of local log-polynomial estimators applied to unimodal or multimodal distributions.
A Model Selection Approach for the Identification of Quantitative Trait Loci in Experimental Crosses
Authors: Broman Karl W. , Speed Terence P. 
Published: March 2003

SummaryWe consider the problem of identifying the genetic loci (called quantitative trait loci (QTLs)) contributing to variation in a quantitative trait, with data on an experimental cross. A large number of different statistical approaches to this problem have been described; most make use of multiple tests of hypotheses, and many consider models allowing only a single QTL. We feel that the problem is best viewed as one of model selection. We discuss the use of model selection ideas to identify QTLs in experimental crosses. We focus on a back-cross experiment, with strictly additive QTLs, and concentrate on identifying QTLs, considering the estimation of their effects and precise locations of secondary importance. We present the results of a simulation study to compare the performances of the more prominent methods.
Estimation of Integrated Squared Density Derivatives from a Contaminated Sample
Authors: Delaigle A. , Gijbels I. 
Published: March 2003

SummaryWe propose a kernel estimator of integrated squared density derivatives, from a sample that has been contaminated by random noise. We derive asymptotic expressions for the bias and the variance of the estimator and show that the squared bias term dominates the variance term. This coincides with results that are available for non-contaminated observations. We then discuss the selection of the bandwidth parameter when estimating integrated squared density derivatives based on contaminated data. We propose a data-driven bandwidth selection procedure of the plug-in type and investigate its finite sample performance via a simulation study.
Estimating the Concentration of Propagules of a Plant Pathogen from Soil Dilution Plate Counts when the Response is Non-linear
Authors: Ridout M. S. , Harris D. C. 
Published: March 2003

SUMMARY
The estimation of micro-organism concentrations from dilution plate data is discussed for situations where expected counts are not proportional to the amount of sample per plate. Aspects of design and analysis are investigated in relation to an alternative non-linear model in which the concentration is given by the slope at the origin. This exponential model generally provides a good fit to available experimental data. Simulations show that estimators based on the model perform well when the response is non-linear and remain reasonably efficient when the response is linear.
Analysis of the Bovine Spongiform Encephalopathy Maternal Cohort Study: Evidence for Direct Maternal Transmission
Authors: Donnelly C. A. , Ghani A. C. , Ferguson N. M. , Wilesmith J. W. , Anderson R. M. 
Published: March 2003

SUMMARY
In an initial exploratory analysis of the bovine spongiform encephalopathy (BSE) maternal cohort study data we demonstrate several confounding effects in the study design. Given these effects, we assess a variety of statistical models to determine the relative contributions of direct maternal transmission of the aetiological agent of BSE and of genetic susceptibility to the observed maternally enhanced risk of BSE in the offspring of affected dams. To control for the substantial between-herd variation in the risk of exposure to the BSE agent, it is essential that analyses take into account the matched pair structure of the data. Maternal exposure is estimated to be most important in animals born within 150 days of disease onset in their dams. The analysis of a full survival likelihood model indicates that the hypothesis of maternal transmission with no genetic variation in susceptibility fits the data significantly better than the hypothesis of genetically variable susceptibility with no maternal transmission. However, models incorporating both maternal transmission and genetically variable susceptibility fit the data significantly better than pure maternal transmission models. Although genetic susceptibility cannot be excluded as the cause of the cohort study results in the absence of detailed genotyping, the analysis of these study data suggest that low level maternal transmission of BSE is, at least in part, responsible for the significantly enhanced risk of BSE in the offspring of affected dams. Similar results indicating significant maternal transmission in the later stages of the dam incubation period are obtained from the independent analysis of data on the dam–offspring relationships among confirmed BSE cases.
Nonparametric and Non-Linear Models and Data Mining in time Series: A Case-Study on the Canadian Lynx Data
Authors: Lin T. C. , Pourahmadi M. 
Published: March 2003

SUMMARY
Nonparametric regression methods are used as exploratory tools for formulating, identifying and estimating non-linear models for the Canadian lynx data, which have attained bench-mark status in the time series literature since the work of Moran in 1953. To avoid the curse of dimensionality in the nonparametric analysis of this short series with 114 observations, we confine attention to the restricted class of additive and projection pursuit regression (PPR) models and rely on the estimated prediction error variance to compare the predictive performance of various (non-)linear models. A PPR model is found to have the smallest (in-sample) estimated prediction error variance of all the models fitted to these data in the literature. We use a data perturbation procedure to assess and adjust for the effect of data mining on the estimated prediction error variances; this renders most models fitted to the lynx data comparable and nearly equivalent. However, on the basis of the mean-squared error of out-of-sample prediction error, the semiparametric model Xt=1.08+1.37Xt−1+f(Xt−2)+et and Tong’s self-exciting threshold autoregression model perform much better than the PPR and other models known for the lynx data.
Bayesian Classification of Neolithic Tools
Authors: Dellaportas Petros 
Published: March 2003

SUMMARY
The classification of Neolithic tools by using cluster analysis enables archaeologists to understand the function of the tools and the technological and cultural conditions of the societies that made them. In this paper, Bayesian classification is adopted to analyse data which raise the question whether the observed variability, e.g. the shape and dimensions of the tools, is related to their use. The data present technical difficulties for the practitioner, such as the presence of mixed mode data, missing data and errors in variables. These complications are overcome by employing a finite mixture model and Markov chain Monte Carlo methods. The analysis uses prior information which expresses the archaeologist’s belief that there are two tool groups that are similar to contemporary adzes and axes. The resulting mixing densities provide evidence that the morphological dimensional variability among tools is related to the existence of these two tool groups.
Modelling Variability in the Branching Structure of Strawberry Inflorescences
Authors: Ridout Martin S. , Morgan Byron J. T. , Taylor David R. 
Published: March 2003

SUMMARY
The branching structure of inflorescences of the cultivated strawberry (Fragaria×ananassa Duch.) is very variable. This paper demonstrates that some aspects of this variability are well described by a simple stochastic model of branching that has two adjustable parameters. The model is shown to provide a good fit to data from a set of almost 700 inflorescences of the cultivar Elsanta, collected over two successive years. For one parameter the maximum likelihood estimator is a moment estimator which is fully efficient even if the detailed branching structure of the inflorescences is not recorded. This parameter provides a convenient summary of branching vigour. The maximum likelihood estimator of the second parameter must be determined iteratively and can be quite inefficient unless the full branching structure is recorded. The model demonstrates that branching structure is affected by the order in which inflorescences emerge on the plant.
The Multivariate Gaussian Tail Model: An Application to Oceanographic Data
Authors: Bortot Paola , Coles Stuart , Tawn Jonathan 
Published: March 2003

SUMMARY
Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea-levels at Newlyn, a port in south-west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea-state process at Newlyn.
A Bayesian Analysis of Fluid Flow in Pipe-Lines
Authors: Rougier Jonathan , Goldstein Michael 
Published: March 2003

SUMMARY
The waterhammer equations are a pair of partial differential equations that describe the behaviour of an incompressible fluid in a pipe-line. We generalize these equations to account for uncertainty in the description of the liquid and the pipe-line, the behaviour of the boundaries of the pipe-line and the method of solution. We illustrate applications of our model to pipe-line design and to realtime pipe-line monitoring, e.g. for detecting leaks, and discuss the general features of our approach to the careful sourcing of uncertainty in deterministic models.
Interpreting Heidegger Across Philosophical Traditions
Authors: Haapala Arto 
Published: March 2003

Heidegger’s philosophy has received radically different readings. These different approaches grow from philosophical differences rooted, at least to some extent, in national philosophical traditions. Although it is not possible any longer to draw strict boundaries between different philosophical traditions by reference to nationality or to language, there certainly are tendencies and points of emphasis that differ depending on the context in which Heidegger is read.There are many different ways of reading Heidegger. I confine myself to two: the orthodox approach and the applicative pragmatist approach. As an example for these approaches I have taken Heidegger’s controversial essay, “The Origin of the Work of Art.”