bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ehrenberg Law-Like Relationship and Anthropometry
Authors: Forcheh N. 
Published: March 2003

SummaryEhrenberg presented the simple law-like relationship log(w) = 0.8h + 0.4 ± 0.04 between the weight and height of children aged 5–13 years. Several researchers have confirmed that this relationship holds, irrespective of the child's ethnic, racial, gender and social class. In anthropometry, a weight–height relationship is used to measure the nutritional status of children. For this purpose, the World Health Organization have adopted the National Center for Health Statistics population as the international reference population. The relationship between the World Health Organization–National Center for Health Statistics anthropometric standards and Ehrenberg law-like relationship is examined. Differences between the weight-for-height relationship in anthropometry and the law-like relationship between weight and height for children are small and can be attributable to functional differences. It is found that an Ehrenberg law-like relationship can be extended to include children who are under 5 years old. Criteria for using the law-like relationship to assess the nutritional status of these children are thus suggested. The criteria are evaluated using anthropometric data of a sample of 513 children from a rural district of Botswana. The results indicate that the proposed method of using the law-like relationship to assess nutritional status is much simpler and at least as reliable as the existing methods in anthropometry.
Ignorability, Sufficiency and Ancillarity
Authors: Heitjan Daniel F. 
Published: March 2003

Summary
This paper explores the relationship between ignorability, sufficiency and ancillarity in the coarse data model of Heitjan and Rubin. Bayes or likelihood ignorability has a natural relationship to sufficiency, and frequentist ignorability an analogous relationship to ancillarity. Weaker conditions, termed observed likelihood sufficiency, observed specific sufficiency and observed ancillarity, expand the concepts to models where the coarsening mechanism is sometimes, but not always, ignorable.
Parameter Neutral Optimum Design for Non-linear Models
Authors: Firth D. , Hinde J. P. 
Published: March 2003

Summary
Some Bayesian approaches to D-optimum design of experiments are considered from the viewpoint of invariance under reparameterization of the underlying statistical model. An invariant criterion is proposed which does not require the detailed specification of a prior, and which is shown to be equivalent to G-optimality under a Jeffreys prior. The methods are applied and discussed in the contexts of exponential decay and quantal response models.
Wavelet Thresholding via A Bayesian Approach
Authors: Abramovich F. , Sapatinas T. , Silverman B. W. 
Published: March 2003

Summary
We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in nonparametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion that is common to most applications. For the prior specified, the posterior median yields a thresholding procedure. Our prior model for the underlying function can be adjusted to give functions falling in any specific Besov space. We establish a relationship between the hyperparameters of the prior model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relationship gives insight into the meaning of the Besov space parameters. Moreover, the relationship established makes it possible in principle to incorporate prior knowledge about the function's regularity properties into the prior model for its wavelet coefficients. However, prior knowledge about a function's regularity properties might be difficult to elicit; with this in mind, we propose a standard choice of prior hyperparameters that works well in our examples. Several simulated examples are used to illustrate our method, and comparisons are made with other thresholding methods. We also present an application to a data set that was collected in an anaesthesiological study.
Understanding Exponential Smoothing Via Kernel Regression
Authors: Gijbels I. , Pope A. , Wand M. P. 
Published: March 2003

Summary
Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.
Exponential Family State Space Models Based on a Conjugate Latent Process
Authors: Vidoni Paolo 
Published: March 2003

Summary
This paper introduces non-linear and non-Gaussian state space models with analytic updating recursions for filtering and prediction. This new class of models involves some well-known results in the theory of exponential models and of exponential dispersion models and the latent process is defined in such a way that both the filtering and the prediction distributions turn out to be conjugate to the observation distribution at each step. The corresponding analytic and inferential properties are investigated and some simple examples are presented.
The Effect of Monte Carlo Approximation on Coverage Error of Double-Bootstrap Confidence Intervals
Authors: Lee S. M. S. , Young G. A. 
Published: March 2003

Summary
A double-bootstrap confidence interval must usually be approximated by a Monte Carlo simulation, consisting of two nested levels of bootstrap sampling. We provide an analysis of the coverage accuracy of the interval which takes account of both the inherent bootstrap and Monte Carlo errors. The analysis shows that, by a suitable choice of the number of resamples drawn at the inner level of bootstrap sampling, we can reduce the order of coverage error. We consider also the effects of performing a finite Monte Carlo simulation on the mean length and variability of length of two-sided intervals. An adaptive procedure is presented for the choice of the number of inner level resamples. The effectiveness of the procedure is illustrated through a small simulation study.
Invariance and Factorial Models
Authors: McCullagh Peter 
Published: March 2003

Summary
Two factors having the same set of levels are said to be homologous. This paper aims to extend the domain of factorial models to designs that include homologous factors. In doing so, it is necessary first to identify the characteristic property of those vector spaces that constitute the standard factorial models. We argue here that essentially every interesting statistical model specified by a vector space is necessarily a representation of some algebraic category. Logical consistency of the sort associated with the standard marginality conditions is guaranteed by category representations, but not by group representations. Marginality is thus interpreted as invariance under selection of factor levels (I-representations), and invariance under replication of levels (S-representations). For designs in which each factor occurs once, the representations of the product category coincide with the standard factorial models. For designs that include homologous factors, the set of S-representations is a subset of the I-representations. It is shown that symmetry and quasi-symmetry are representations in both senses, but that not all representations include the constant functions (intercept). The beginnings of an extended algebra for constructing general I-representations is described and illustrated by a diallel cross design.
On Variable Bandwidth Selection in Local Polynomial Regression
Authors: Doksum Kjell , Peterson Derick , Samarov Alex 
Published: March 2003

Summary
The performances of data-driven bandwidth selection procedures in local polynomial regression are investigated by using asymptotic methods and simulation. The bandwidth selection procedures considered are based on minimizing ‘prelimit’ approximations to the (conditional) mean-squared error (MSE) when the MSE is considered as a function of the bandwidth h. We first consider approximations to the MSE that are based on Taylor expansions around h=0 of the bias part of the MSE. These approximations lead to estimators of the MSE that are accurate only for small bandwidths h. We also consider a bias estimator which instead of using small h approximations to bias naïvely estimates bias as the difference of two local polynomial estimators of different order and we show that this estimator performs well only for moderate to large h. We next define a hybrid bias estimator which equals the Taylor-expansion-based estimator for small h and the difference estimator for moderate to large h. We find that the MSE estimator based on this hybrid bias estimator leads to a bandwidth selection procedure with good asymptotic and, for our Monte Carlo examples, finite sample properties.
Parameterization and Inference for Nonparametric Regression Problems
Authors: Jiang Wenxin , Kipnis Victor , Midthune Douglas , Carroll Raymond J. 
Published: March 2003

Summary
We consider local likelihood or local estimating equations, in which a multivariate function Θ(·) is estimated but a derived function λ(·) of Θ(·) is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of Θ(·). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function λ(·) is facilitated without a loss of power by reparameterization so that λ(·) is itself a component of Θ(·). Our approach is in contrast with the standard practice of choosing Θ(·) for convenience and allowing λ(·)&gt; to be a non-linear function of Θ(·). The methods are applied to an important data set in nutritional epidemiology.
Influence Diagnostics and Outlier Tests for Semiparametric Mixed Models
Authors: Fung Wing-Kam , Zhu Zhong-Yi , Wei Bo-Cheng , He Xuming 
Published: March 2003

SummarySemiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.
Bandwidth Selection for Local Linear Regression Smoothers
Authors: Hengartner Nicolas W. , Wegkamp Marten H. , Matzner-Løber Eric 
Published: March 2003

SummaryThe paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.
The Evaluation of General Non-Centred Orthant Probabilities
Authors: Miwa Tetsuhisa , Hayter A. J. , Kuriki Satoshi 
Published: March 2003

SummaryThe evaluation of the cumulative distribution function of a multivariate normal distribution is considered. The multivariate normal distribution can have any positive definite correlation matrix and any mean vector. The approach taken has two stages. In the first stage, it is shown how non-centred orthoscheme probabilities can be evaluated by using a recursive integration method. In the second stage, some ideas of Schläfli and Abrahamson are extended to show that any non-centred orthant probability can be expressed as differences between at most (m−1)! non-centred orthoscheme probabilities. This approach allows an accurate evaluation of many multivariate normal probabilities which have important applications in statistical practice.
Comparing Two Measures of Creatinine Clearance: An Application of Errors-in-Variables and Bootstrap Techniques
Authors: Oman Samuel D. , Meir Natalia , Halm Nissim 
Published: March 2003

SUMMARY
Two indices of creatinine clearance (an index of kidney function) are compared on a group of cancer patients who underwent chemotherapy with a potentially nephrotoxic drug. The standard index, measured creatinine clearance MCC, is cumbersome to use, whereas the more convenient alternative, estimated creatinine clearance ECC, has not yet been conclusively evaluated on cancer patients. We conclude that under certain clinical conditions ECC and MCC are identically calibrated for males, but not for females, and we obtain estimated true and false positive rates for assessing the use of ECC instead of MCC as a diagnostic tool. We use a model that is formally equivalent to an errors-in-variables model with (unbalanced) repeated observations and correlated measurement errors. The bootstrap is used to obtain standard errors and confidence limits.
Estimating the Propagation Rate of a Viral Infection of Potato Plants via Mixtures of Regressions
Authors: Turner T. Rolf 
Published: March 2003

SUMMARY
A problem arising from the study of the spread of a viral infection among potato plants by aphids appears to involve a mixture of two linear regressions on a single predictor variable. The plant scientists studying the problem were particularly interested in obtaining a 95% confidence upper bound for the infection rate. We discuss briefly the procedure for fitting mixtures of regression models by means of maximum likelihood, effected via the EM algorithm. We give general expressions for the implementation of the M-step and then address the issue of conducting statistical inference in this context. A technique due to T. A. Louis may be used to estimate the covariance matrix of the parameter estimates by calculating the observed Fisher information matrix. We develop general expressions for the entries of this information matrix. Having the complete covariance matrix permits the calculation of confidence and prediction bands for the fitted model. We also investigate the testing of hypotheses concerning the number of components in the mixture via parametric and ‘semiparametric’ bootstrapping. Finally, we develop a method of producing diagnostic plots of the residuals from a mixture of linear regressions.
Optimal Sampling Times in Population Pharmacokinetic Studies
Authors: Stroud Jonathan R. , Müller Peter , Rosner Gary L. 
Published: March 2003

SUMMARY
We propose a simulation-based approach to decision theoretic Bayesian optimal design. The underlying probability model is a population pharmacokinetic model which allows for correlated responses (drug concentrations) and patient-to-patient heterogeneity. We consider the problem of choosing sampling times for the anticancer agent paclitaxel, using criteria related to the total area under the curve, the time above a critical threshold and the sampling cost.
Validation of Surrogate end Points in Multiple Randomized Clinical Trials with Failure Time end Points
Authors: Burzykowski Tomasz , Molenberghs Geert , Buyse Marc , Geys Helena , Renard Didier 
Published: March 2003

SUMMARY
Before a surrogate end point can replace a final (true) end point in the evaluation of an experimental treatment, it must be formally ‘validated’. The validation will typically require large numbers of observations. It is therefore useful to consider situations in which data are available from several randomized experiments. For two normally distributed end points Buyse and co-workers suggested a new definition of validity in terms of the quality of both trial level and individual level associations between the surrogate and true end points. This paper extends this approach to the important case of two failure time end points, using bivariate survival modelling. The method is illustrated by using two actual sets of data from cancer clinical trials.
Modelling Change in Cognitive Understanding with Finite Mixtures
Authors: Thomas Hoben , Hettmansperger Thomas P. 
Published: March 2003

SUMMARY
Psychological theories often posit the existence of several different states. Individuals are viewed as belonging to one of the states at a given age, but with development pass to another state. A main problem in evaluating such theories is representing the transition from one state to another over age. A stochastic transition framework is proposed which should be useful in many different settings. The model is illustrated with data from a cognitive development task.
Bayesian Modelling of Catch in a North-West Atlantic Fishery
Authors: Fernández Carmen , Ley Eduardo , Steel Mark F. J. 
Published: March 2003

SummaryWe model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.
Correlation-Adjusted Estimation of Sensitivity and Specificity of Two Diagnostic Tests
Authors: Georgiadis Marios P. , Johnson Wesley O. , Gardner Ian A. , Singh Ramanpreet 
Published: March 2003

SummaryModels for multiple-test screening data generally require the assumption that the tests are independent conditional on disease state. This assumption may be unreasonable, especially when the biological basis of the tests is the same. We propose a model that allows for correlation between two diagnostic test results. Since models that incorporate test correlation involve more parameters than can be estimated with the available data, posterior inferences will depend more heavily on prior distributions, even with large sample sizes. If we have reasonably accurate information about one of the two screening tests (perhaps the standard currently used test) or the prevalences of the populations tested, accurate inferences about all the parameters, including the test correlation, are possible. We present a model for evaluating dependent diagnostic tests and analyse real and simulated data sets. Our analysis shows that, when the tests are correlated, a model that assumes conditional independence can perform very poorly. We recommend that, if the tests are only moderately accurate and measure the same biological responses, researchers use the dependence model for their analyses.
It’s Not What You Pay it’s the Way that You Pay it and that’s What Gets Results: Jockeys’ Pay and Performance
Authors: Fernie Sue , Metcalf David 
Published: March 2003

Management scholars and economists have recently set out the requirements of a system to elicit good performance when it is necessary to align the interests of the principal and agent. We analyse pay and performance in an occupation — jockeys — replete with moral hazard possibilities. We are able to do this because, most unusually, a measure of pure individual performance exists for an unbalanced panel of some 50 individuals for 8 years. Three hypotheses are tested. First, in line with classic agency theory, we expect monitoring mechanisms and incentive contracts to be used to align the interests of principals and agents. Second, pay and performance should be positively associated, subject to the first hypothesis being confirmed. Third, a limited number of jockeys were paid via an alternative mechanism involving very large non‐contingent retainer fees. This serves as our counterfactual payment system. In line with agency theory we expect worse performance under such a system than under an incentive contract. The three hypotheses are confirmed: incentive contracts generate superior performance to non‐contingent payment systems. Our evidence suggests that ‘it’s not what you pay it’s the way that you pay it … and that’s what gets results’.It is maddening that society confers its blessings on traditional academic pursuits but views the study of horseracing as utter frivolity (Beyer, 1983).
Policymakers’ Revealed Preferences and the Output–Inflation Variability Trade–off: Implications for the European System of Central Banks
Authors: Cecchetti Stephen G. , McConnell Margaret M. , Perez–Quiros Gabriel 
Published: March 2003

This paper explores two aspects of the conduct of monetary policy under a monetary union. First, even if the preferences of policymakers over inflation and output variability are identical across member countries, differences in economic structure will mean different desired policy responses to even a common shock. Second, policymakers may be forced to make important concessions in their preferences over inflation and output variability. To examine these issues, in this paper we estimate the objective functions that the European national central banks were implicitly maximizing over the 15 or so years prior to monetary union, as well as the slopes of the inflation–output variability trade–off in each country.While the slopes of the trade–offs vary dramatically across countries, the objective functions are quite similar, with most countries having weights in excess of three–quarters on inflation variability and less than one–quarter on output variability. Our findings suggest that the concessions (in terms of preferences over output and inflation variability) that current inflation–targeting countries such as the UK and Sweden would have to make on accession to the European Monetary Union (EMU) are likely to be minimal. On the other hand, the differences in economic structure across the Eurosystem countries might make it difficult to formulate a common policy even in the face of common goals, suggesting that there may still be significant costs to joining for countries currently outside the EMU.