bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frequentist and Bayesian Bartlett Correction of Test Statistics Based on Adjusted Profile Likelihoods
Authors: Diciccio Thomas J. , Stern Steven E. 
Published: December 2018

SUMMARY
For inference about a multidimensional parameter ψ in the presence of nuisance parameters, the usual log-profile-likelihood function M(ψ) is often replaced by an objective function M̄(ψ) = M(ψ) + B(ψ), where B(ψ) is Op(1). It is shown in this paper that under mild conditions on the adjustment function B(ψ) both frequentist and Bayesian Bartlett correction are possible for the adjusted likelihood ratio statistic arising from M̄(ψ). General formulae for the Bartlett adjustment factors are developed, and these formulae are evaluated for some specific adjustment functions B(ψ). Furthermore, conditions under which the frequentist and Bayesian adjustment factors agree to order Op(n –3/2) are considered. In particular, prior probability density functions for which the highest posterior density region of posterior probability content 1 – α has frequentist coverage level 1 – α + O(n –2) are characterized, and similar characterizations are given for likelihood-based regions.
Neural Networks and Related Methods for Classification
Authors: Ripley B. D. 
Published: December 2018

SUMMARY
Feed-forward neural networks are now widely used in classification problems, whereas non-linear methods of discrimination developed in the statistical field are much less widely known. A general framework for classification is set up within which methods from statistics, neural networks, pattern recognition and machine learning can be compared. Neural networks emerge as one of a class of flexible non-linear regression methods which can be used to classify via regression. Many interesting issues remain, including parameter estimation, the assessment of the classifiers and in algorithm development.
Estimation in the Mean Residual Life Regression Model
Authors: Maguluri Gangaji , Zhang Cun-Hui 
Published: December 2018

SUMMARY
The proportional mean residual life model was originally proposed by Oakes and Dasu. This model can be extended to a regression model with explanatory variables. We consider the problem of estimation of the regression parameter in this general model. We investigate two types of estimator, of which one is based on the maximum likelihood equation of the exponential regression model, and the other is based on the underlying proportional hazards structure of the model and Cox's estimating equation. We show that these estimators are consistent and asymptotically normal. We compare their performance by simulations.
Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models
Authors: Frühwirth-Schnatter Sylvia 
Published: December 2018

SUMMARY
It is shown how to discriminate between different linear Gaussian state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. A practical implementation of this procedure requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by Markov chain Monte Carlo methods. An application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail.
Predictive Model Selection
Authors: Laud Purushottam W. , Ibrahim Joseph G. 
Published: December 2018

SUMMARY
We consider the problem of selecting one model from a large class of plausible models. A predictive Bayesian viewpoint is advocated to avoid the specification of prior probabilities for the candidate models and the detailed interpretation of the parameters in each model. Using criteria derived from a certain predictive density and a prior specification that emphasizes the observables, we implement the proposed methodology for three common problems arising in normal linear models: variable subset selection, selection of a transformation of predictor variables and estimation of a parametric variance function. Interpretation of the relative magnitudes of the criterion values for various models is facilitated by a calibration of the criteria. Relationships between the proposed criteria and other well-known criteria are examined.
On the Influence Functions of Certain Bivariate Medians
Authors: Niinimaa A. , Oja H. 
Published: December 2018

SUMMARY
The asymptotic efficiency, influence function and breakdown properties of the multivariate median proposed by Oja are investigated and comparisons are made with two other generalizations of the median: the spatial median and the vector of marginal medians. It is shown that, for distributions with finite expectations, the Oja median has a bounded influence function although this median has 0% breakdown. For circular distributions, the influence functions of the Oja median and the spatial median coincide. The Oja median is then as efficient as the spatial median in the circular case but strictly better for any other elliptical case. It is noteworthy that the spatial median, however, has the best possible 50% breakdown. This phenomenon is analysed in the paper: it is shown that the finite sample breakdown point of the Oja median depends on the dispersion of the corrupted observations.
A Geometric Approach to Optimal Design for One-Parameter Non-Linear Models
Authors: Haines Linda M. 
Published: December 2018

SUMMARY
A geometric framework for constructing optimal Bayesian designs and maximin designs for non-linear models with a single unknown parameter and a prior distribution on that parameter, which is restricted in that it comprises exactly two points of support, is presented. The approach is illustrated by means of selected examples involving logistic regression and the simple exponential model, and its applicability to the construction of optimal designs for models with uncontrolled variation and to model robust designs is also demonstrated. In addition, the method is shown to provide some valuable insights into the general properties of optimal Bayesian designs for non-linear models.
A Class of Bayesian Models for Optimal Exploration
Authors: Glazebrook K. D. , Boys R. J. 
Published: December 2018

SUMMARY
Each of several locations contains an unknown number of objects of value. A single search of a location will discover some of these objects which are then removed. Discoveries yield rewards. A ‘distribution of effort’ problem is posed concerning how to explore the locations optimally—i.e. to maximize the expected return from discoveries made. This is formulated as a Bayes sequential decision problem for which index policies are optimal. A natural simple case is one in which, conditionally on the number of undiscovered objects at a location N, the number of discoveries made in a single search is binomial B(N, p) where p is a detection rate. For this case, we can gain considerable insight into how model structure relates to policy structure. The tail behaviour of the priors for the number of objects at each location plays an important role.
The Empirical Process Approach for Semiparametric Two-Sample Models with Heterogeneous Treatment Effect
Authors: Hsieh Fushing 
Published: December 2018

SUMMARY
Two types of semiparametric model are proposed for comparing two samples with possibly heterogeneous treatment effects. One model is a two-sample location-scale model which assumes that the Q-Q-plot of the two distributions involved is linear; the other is the class of two-sample transformation models which assume parametric forms for a probability plot—the receiver operating characteristic curve. The empirical process approach is used to construct strong approximations for the empirical curves of both types of plot and a convenient generalized least squares (GLS) estimator is derived. The approximate finite sample precision of the estimators can be easily evaluated. Asymptotically, the GLS estimator is shown to be efficient for the first model. A difficulty involved in developing the theory of asymptotic efficiency is discussed for the transformation models. The GLS estimator is shown numerically to approach the Fisher information bound for the proportional hazards model.
Inferences from Multinomial Data: Learning About a Bag of Marbles
Authors: Walley Peter 
Published: December 2018

SUMMARY
A new method is proposed for making inferences from multinomial data in cases where there is no prior information. A paradigm is the problem of predicting the colour of the next marble to be drawn from a bag whose contents are (initially) completely unknown. In such problems we may be unable to formulate a sample space because we do not know what outcomes are possible. This suggests an invariance principle: inferences based on observations should not depend on the sample space in which the observations and future events of interest are represented. Objective Bayesian methods do not satisfy this principle. This paper describes a statistical model, called the imprecise Dirichlet model, for drawing coherent inferences from multinomial data. Inferences are expressed in terms of posterior upper and lower probabilities. The probabilities are initially vacuous, reflecting prior ignorance, but they become more precise as the number of observations increases. This model does satisfy the invariance principle. Two sets of data are analysed in detail. In the first example one red marble is observed in six drawings from a bag. Inferences from the imprecise Dirichlet model are compared with objective Bayesian and frequentist inferences. The second example is an analysis of data from medical trials which compared two treatments for cardiorespiratory failure in newborn babies. There are two problems: to draw conclusions about which treatment is more effective and to decide when the randomized trials should be terminated. This example shows how the imprecise Dirichlet model can be used to analyse data in the form of a contingency table.
Estimation of Population Exposure in Ecological Studies
Authors: Plummer Martyn , Clayton David 
Published: December 2018

SUMMARY
This paper discusses design issues in 'ecological studies' —epidemiological studies in which the relationship between disease and behavioural and environmental determinants is studied at the population rather than the individual level. The number of study populations has little relevance beyond a certain point, the power and precision being limited by the total number of disease events and by the size of the sample surveys used to estimate the distributions of determinants within populations. In most circumstances, optimal design requires the size of the sample surveys in each population to be related to the number of disease events which will occur in it, and for sampling to be stratified by age and/or sex.
Discriminant Analysis by Gaussian Mixtures
Authors: Hastie Trevor , Tibshirani Robert 
Published: December 2018

SUMMARY
Fisher-Rao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in non-normal settings, especially when the classes are clustered. Low dimensional views are an important by-product of LDA—our new techniques inherit this feature. We can control the within-class spread of the subclass centres relative to the between-class spread. Our technique for fitting these models permits a natural blend with nonparametric versions of LDA.
Information Bias and Adjusted Profile Likelihoods
Authors: Diciccio Thomas J. , Stern Steven E. , Martin Michael A. , Young G. Alastair 
Published: December 2018

SUMMARY
The bias and information bias of the ordinary profile score statistic are both typically of order O(1). Several additive adjustments to the profile score statistic that reduce its bias to order O(n −1) have been proposed. In certain situations, the information bias of these adjusted profile score statistics is also reduced to order O(n −1); specifically, the modified profile likelihood of Barndorff-Nielsen yields an adjusted profile score statistic having both reduced bias and reduced information bias. In general, however, a bias reducing adjustment to the profile score statistic will not automatically reduce the order of the information bias as well. In this paper, an analytical formula is obtained for the information bias of a general bias-adjusted profile score statistic. This formula is used to compare bias reducing adjustments to the profile score statistic, as well as to construct further adjustments that reduce the information bias to O(n −1). Several examples are presented to illustrate use of the formula for information bias. In particular, the information bias formula may be utilized in a criterion for choosing between orthogonal parameterizations for the conditional profile likelihood of Cox and Reid.
Regression Shrinkage and Selection Via the Lasso
Authors: Tibshirani Robert 
Published: December 2018

SUMMARY
We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.
Estimation for Censored Exponential Data When the Censoring Times are Subject to Error
Authors: Phillips M. J. , Sweeting T. J. 
Published: December 2018

SUMMARY
The problem of estimating the parameter of the negative exponential distribution with censored data may be complicated by a lack of complete knowledge of the censoring times. The effect of ignoring these errors in the censoring times rather than using a complete maximum likelihood estimation approach is investigated and it is shown that the loss in efficiency is generally small for the inverted gamma model considered in this paper. The theory is supplemented by some numerical results based on an example of failure data.
Influence of processing conditions on some physical characteristics of vegetable oil expressed mechanically from Pentaclethra macrophylla Benth kernels: Response surface approach
Authors: Ogunlade Clement A. , Aremu Ademola K. 
Published: December 2018

AbstractThe global increase in demand for vegetable oils due to their nutritional, pharmaceutical, and industrial importance necessitates investigation on exploitation of new oil bearing crops. This study was undertaken to determine the influence of processing conditions on physical properties of oil expressed mechanically from African oil bean kernels. Central composite rotatable design of response surface methodology (RSM) at 3‐factors, 5‐levels was adopted; independent variables considered were: moisture content (MC; 8, 10, 12, 14, and 16% dry basis), heating duration (HD; 5, 10, 15, 20, and 25 min), and heating temperature (HT; 50, 70, 90, 110, 130 °C) while dependent variables (physical characteristics) include: oil impurities, density, kinematic viscosity, dynamic viscosity, specific gravity, refractive index, and color; all determined using standard methods. Optimum values were obtained using RSM. Optimum oil quality (0.044%m/m impurity, 0.92 kg/m3 density, 43.86 mm2/s kinematic viscosity, 40.8 mPa s dynamic viscosity, 0.9 specific gravity, 1.469 refractive index, and 7.7 color units) was obtained at 8% db, 11.7 min HD, and 59.3 °C HT. The quality of the expressed oil falls within permissible limits for edible oils and high nutritional importance. The processing conditions influenced the quality of the mechanically expressed African oil bean oil.Practical applicationsAfrican oil bean is an underutilized plant with potentials for meeting increasing global demand for vegetable oil for nutritional and industrial importance. The application/usage of oil is dependent on its quality attributes which is otherwise dependent on processing parameters like moisture content, heating duration, and heating temperature. These factors need to be controlled during oil expression processes to actually the optimum conditions that will yield good quality oil. The degree of influence of the parameters on oil quality is a useful data to vegetable oil processors, food, and agricultural engineers. The quality parameters of the oil were determined and compared with international standard parameters. Mathematical models were developed to predict the yield and quality attributes of expressed oil from African oil bean kernels at various processing conditions.
Pharmacotherapy in Older Adults with Cardiovascular Disease: Report from an American College of Cardiology, American Geriatrics Society, and National Institute on Aging Workshop
Authors: Schwartz Janice B. , Schmader Kenneth E. , Hanlon Joseph T. , Abernethy Darrell R. , Gray Shelly , Dunbar‐Jacob Jacqueline , Holmes Holly M. , Murray Michael D. , Roberts Robert , Joyner Michael , Peterson Josh , Lindeman David , Tai‐Seale Ming , Downey Laura , Rich Michael W. 
Published: December 2018

OBJECTIVESTo identify the top priority areas for research to optimize pharmacotherapy in older adults with cardiovascular disease (CVD).DESIGNConsensus meeting.SETTINGMultidisciplinary workshop supported by the National Institute on Aging, the American College of Cardiology, and the American Geriatrics Society, February 6–7, 2017.PARTICIPANTSLeaders in the Cardiology and Geriatrics communities, (officers in professional societies, journal editors, clinical trialists, Division chiefs), representatives from the NIA; National Heart, Lung, and Blood Institute; Food and Drug Administration; Centers for Medicare and Medicaid Services, Alliance for Academic Internal Medicine, Patient‐Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, pharmaceutical industry, and trainees and early career faculty with interests in geriatric cardiology.MEASUREMENTSSummary of workshop proceedings and recommendations.RESULTSTo better align older adults’ healthcare preferences with their care, research is needed to improve skills in patient engagement and communication. Similarly, to coordinate and meet the needs of older adults with multiple comorbidities encountering multiple healthcare providers and systems, systems and disciplines must be integrated. The lack of data from efficacy trials of CVD medications relevant to the majority of older adults creates uncertainty in determining the risks and benefits of many CVD therapies; thus, developing evidence‐based guidelines for older adults with CVD is a top research priority. Polypharmacy and medication nonadherence lead to poor outcomes in older people, making research on appropriate prescribing and deprescribing to reduce polypharmacy and methods to improve adherence to beneficial therapies a priority.CONCLUSIONThe needs and circumstances of older adults with CVD differ from those that the current medical system has been designed to meet. Optimizing pharmacotherapy in older adults will require new data from traditional and pragmatic research to determine optimal CVD therapy, reduce polypharmacy, increase adherence, and meet person‐centered goals. Better integration of the multiple systems and disciplines involved in the care of older adults will be essential to implement and disseminate best practices. J Am Geriatr Soc 67:371–380, 2019.
Determination of the Minimal Important Change in the Life‐Space Assessment
Authors: Kennedy Richard E. , Almutairi Marzouq , Williams Courtney P. , Sawyer Patricia , Allman Richard M. , Brown Cynthia J. 
Published: December 2018

Background/ObjectivesThe University of Alabama at Birmingham (UAB) Life‐Space Assessment (LSA) is a widely used measure of community mobility. To assist clinicians and researchers with assessing the significance of changes in scores, we determined the minimal important change associated with a change in health status.SettingHomes of community‐dwelling older adults.ParticipantsA total of 419 African American and non‐Hispanic white adults 75 years and older participating in the UAB Study of Aging II, a longitudinal epidemiological study across the state of Alabama.InterventionNone.MeasurementsLinear mixed models were used to compare change in LSA scores over 1‐month intervals (N = 9712) between participants reporting improvement, no change, or decline in activities of daily living walking scores, accounting for the correlation among scores for the same participant over time.ResultsA decline in walking status was associated with a mean decrease in LSA scores of 2.93 points (95% confidence interval [CI] = 1.69‐4.17 points), indicating lower mobility. An improvement in walking status was associated with a mean increase in LSA scores of 2.51 points (95% CI = 1.26‐3.77 points), indicating higher mobility.ConclusionA change in LSA scores of five or more is clinically important, exceeding the 95% CI for the change in LSA associated with change in walking status. Changes exceeding this threshold should prompt further investigation by providers with a goal of preserving mobility. J Am Geriatr Soc 67:565–569, 2019.
The Effect of Ceramic Type and Background Color on Shade Reproducibility of All‐Ceramic Restorations
Authors: Al Hamad Khaled Q. , Obaidat Ismaeel I. , Baba Nadim Z. 
Published: December 2018

AbstractPurposeTo investigate the effects of background color on shade reproduction using CAD/CAM zirconia and lithium disilicate ceramics.Materials and MethodsA plastic tooth was prepared for an all‐ceramic crown and scanned. Tooth‐shaped light background dies were manufactured. Full‐contour crowns were milled from translucent zirconia (ZT) and low translucency lithium disilicates (E [LT]). Copings from opaque zirconia (Z) and medium opacity lithium disilicate (E[MO]) ceramics were milled and layered with veneering porcelain to full contour. Metal copings were used to produce a dark background. Specimens were divided into 8 groups with 3 variables: background color, ceramic type, and translucency. Crowns were cemented using composite resin cement. Color was measured with a spectrophotometer. Color difference (∆E) was calculated using the CIEDE2000 formula between the specimens and the target shade, A1. Statistical analysis was performed using a nested design 3‐way ANOVA and Tukey multiple comparisons.ResultsThe closest ∆E to target shade was produced by E (LT) (2.13 ± 0.19) on the dark background. This increased significantly to 2.90 ± 0.19 on the light background (p = 0.03). The e.max (MO) groups significantly increased (p = 0.001) to 4.40 ± 0.22 and 4.47 ± 0.4, (p = 1.00) for the dark and light backgrounds, respectively. ∆E for the zirconia groups were higher and ranged from 4.85 ± 0.48 to 5.60 ± 0.48 in the ZT groups (p = 0.04) and 6.5 ± 0.82 to 7.75 ± 0.53 (p = 0.001) for Z groups from dark to light backgrounds. There was an overall lack of chromaticity between the specimens and the target shade A1ConclusionsCeramic color was affected by ceramic type, background, and translucency. Lithium disilicate appeared to be better than zirconia in shade reproduction. Less‐opaque materials matched the target shade better, while opaque materials and low translucencies were more suitable for dark backgrounds.