bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimality and Robustness to the Unavailability of Blocks in Block Designs
Authors: Bhaumik Dulal K. , Whittinghill Dexter C.
Published: December 2018

This paper investigates the optimality of designs derived from binary, variance-balanced incomplete block designs (BIBDs) by removing t equal-sized blocks which are not necessarily disjoint. It is shown that the optimal design is derived by removing blocks which have disjoint sets of treatments, and the worst design is derived by removing identical blocks. For BIBDs and t = 2 all resulting designs are ordered. The implication for competing BIBDs which have the same parameters v, b and k but different support sizes (numbers of distinct blocks) is addressed. Optimality and robustness are measured through the non-zero eigenvalues of the C-matrix of the design missing observations using the universal optimality criteria of Bondar.
A Bivariate Signed Rank Test for the Two-Sample Location Problem
Authors: Peters Dawn , Randles Ronald H.
Published: December 2018

A signed rank affine invariant test for the difference in location between two elliptically symmetric populations is proposed. Asymptotic properties of the test are developed under the null hypothesis and under contiguous alternatives. The proposed statistic is compared with three of its competitors using Monte Carlo studies and Pitman asymptotic relative efficiencies. It is seen that the signed rank statistic introduced is especially powerful when the underlying distribution is light tailed. For heavy-tailed distributions a bivariate test due to Mardia is preferred. However, the proposed statistic performs better than Hotelling's T  2-statistic for both light- and heavy-tailed populations, performing almost as well as Hotelling's T  2-statistic under bivariate normality.
Some Tools for Functional Data Analysis
Authors: Ramsay J. O. , Dalzell C. J.
Published: December 2018

Multivariate data analysis permits the study of observations which are finite sets of numbers, but modern data collection situations can involve data, or the processes giving rise to them, which are functions. Functional data analysis involves infinite dimensional processes and/or data. The paper shows how the theory of L-splines can support generalizations of linear modelling and principal components analysis to samples drawn from random functions. Spline smoothing rests on a partition of a function space into two orthogonal subspaces, one of which contains the obvious or structural components of variation among a set of observed functions, and the other of which contains residual components. This partitioning is achieved through the use of a linear differential operator, and we show how the theory of polynomial splines can be applied more generally with an arbitrary operator and associated boundary constraints. These data analysis tools are illustrated by a study of variation in temperature–precipitation patterns among some Canadian weather-stations.
Percentage Points of Likelihood Ratio Tests for Threshold Autoregression
Authors: Chan K. S.
Published: December 2018

Likelihood ratio tests for threshold autoregression have been considered by Chan, and Chan and Tong. However, except for the simplest case, percentage points of the (asymptotic) null distribution of the test statistic have not been tabulated. The purpose of this paper is to tabulate the approximate upper percentage points for the general case. This is done via the Poisson clumping heuristic developed by Aldous. Monte Carlo experiments show that the approximation is quite good. As an illustration, the test is then applied to two data sets.
Comparison between the Conditional Likelihood Ratio and the Usual Likelihood Ratio Tests
Authors: Mukerjee Rahul
Published: December 2018

This paper compares, under contiguous alternatives, the conditional likelihood ratio test of Cox and Reid with the usual likelihood ratio test on the basis of second-order power. It is seen that although the power of the former is identical, up to the second order, with that of the likelihood ratio test with known nuisance parameter, it does not hold with the latter. This provides a justification for the use of the conditional likelihood ratio test, rather than the usual likelihood ratio test, in the presence of a nuisance parameter.
Gittins Indices and Oil Exploration
Authors: Benkherouf L. , Glazebrook K. D. , Owen R. W.
Published: December 2018

An earlier study by Benkherouf and Bather considered the question of when the results of oil exploration in a single area were sufficiently discouraging to render it optimal to stop drilling altogether. This stopping problem is now considerably elaborated by the inclusion of a choice between several areas in which to explore. Our oil exploration problem is modelled as a Bayesian sequential decision problem with a Markovian structure. Optimal drilling strategies are determined by Gittins indices. These indices also play an important part in the evaluation of heuristics.
Prediction for Seemingly Unrelated Regressions
Authors: Percy David F.
Published: December 2018

The seemingly unrelated regressions model is described and analysed from a Bayesian perspective. The predictive density for this model cannot, in general, be evaluated analytically. Two approximations are proposed and investigated. These are Gibbs sampling and a first-order approximation based on a Bayes estimate of the precision matrix. These approximations are then compared on simulated data and both appear to give good results. Extensions to allow for missing data are also discussed.
Modelling Probabilistic Agent Opinion
Authors: West Mike , Crosse Jo
Published: December 2018

A single agent (an individual, expert or model) provides a decision maker with probabilistic information partially or completely describing the agent's opinion about a collection of uncertain quantities or events. This paper discusses ways in which the decision maker may model the agent's opinion to provide rules for updating his own probability for a related event or random quantity of particular interest. Concepts discussed include the relevance of the agent's information and experience, the accord between the agent and decision maker in terms of common or conflicting information and calibration of probability assessments. New theory develops and extends that of Genest and Schervish, requiring only a partial specification of the decision maker's prior over the agent's opinion. Several illustrative examples are developed.
Linear Filters and Non-Linear Systems
Authors: Broomhead D. S. , Huke J. P. , Muldoon M. R.
Published: December 2018

It has been asserted in the literature that the low pass filtering of time series data may lead to erroneous results when calculating attractor dimensions. Here we prove that finite order, non-recursive filters do not have this effect. In fact, a generic, finite order, non-recursive filter leaves invariant all the quantities that can be estimated by using embedding techniques such as the method of delays.
Empirical Functionals and Efficient Smoothing Parameter Selection
Authors: Hall Peter , Johnstone Iain
Published: December 2018

A striking feature of curve estimation is that the smoothing parameter ĥ  0, which minimizes the squared error of a kernel or smoothing spline estimator, is very difficult to estimate. This is manifest both in slow rates of convergence and in high variability of standard methods such as cross-validation. We quantify this difficulty by describing nonparametric information bounds and exhibit asymptotically efficient estimators of ĥ  0 that attain the bounds. The efficient estimators are substantially less variable than cross-validation (and other current procedures) and simulations suggest that they may offer improvements at moderate sample sizes, at least in terms of minimizing the squared error. The key is a stochastic decomposition of the empirical functional ĥ  0 in terms of a smooth quadratic functional of the unknown curve. Examples include the estimation of densities, regression functions and continuous signals in Gaussian white noise.
Collapsibility of Relative Risk in Contingency Tables with a Response Variable
Authors: Geng Zhi
Published: December 2018

We consider the collapsibility of relative risks in contingency tables with a polytomous response variable. Simple collapsibility of relative risks means that they are consistent over all levels of a collapsed variable and also coincide with the corresponding marginal relative risk. Strong collapsibility of relative risks means that they retain simple collapsibility no matter how the variable is partially collapsed. We present necessary and sufficient conditions for simple and strong collapsibilities of relative risks.
Asymptotically Efficient Allocation Rules for Two Bernoulli Populations
Authors: Li Zhaohai , Zhang Cun-Hui
Published: December 2018

Given two Bernoulli populations with unknown means, we consider the problem of sampling x  1, x  2, ... sequentially from these populations such as to maximize the expected value of the sum S  n = x  1 + x  2 + ... + x  n as n → ∞. We obtain an asymptotically efficient rule. Simulation studies show that it performs well for moderate values of n.
Characterizing a Joint Probability Distribution by Conditionals
Authors: Gelman Andrew , Speed T. P.
Published: December 2018

We derive conditions under which a set of conditional and marginal probability distributions will uniquely specify an all-positive joint distribution. Our theoretical result may yield insights into the construction and simulation of multivariate probability models.
A Logic of Probability, with Application to the Foundations of Statistics
Authors: Vovk V. G.
Published: December 2018

A simple example shows that the classical theory of probability implies more than one can deduce via Kolmogorov's calculus of probability. Developing Dawid's ideas I propose a new calculus of probability which is free from this drawback. This calculus naturally leads to a new interpretation of probability. I argue that attempts to create a general empirical theory of probability should be abandoned and we should content ourselves with the logic of probability establishing relations between probabilistic theories and observations. My approach to the logic of probability is based on a variant of Ville's principle of the excluded gambling strategy. In addition to the classical theory of probability this approach is applied to the probabilistic theories provided by the problem of testing validity of probability forecasts and by statistical models.
Approximating Posterior Distributions by Mixtures
Authors: West Mike
Published: December 2018

Kernel density estimation techniques are used to smooth simulated samples from importance sampling function approximations to posterior distributions, resulting in revised approximations that are mixtures of standard parametric forms, usually multivariate normal or T-distributions. Adaptive refinement of such mixture approximations involves repeating this process to home-in successively on the posterior. In fairly low dimensional problems, this provides a general and automatic method of approximating posteriors by mixtures, so that marginal densities and other summaries may be easily computed. This is discussed and illustrated, with comment on variations and extensions suited to sequential Bayesian updating of Monte Carlo approximations, an area in which existing and alternative numerical methods are difficult to apply.
The Assumptions on Which Causal Inferences Rest
Authors: Stone Richard
Published: December 2018

Statisticians commonly make causal inferences from the results of randomized experiments, but usually question causal inferences from observational studies on the grounds that untestable assumptions are required. This paper explains the basis for this situation and examines, within quite a general framework, various assumptions that appear in the literature. It is demonstrated how the role of each assumption is intimately related to the sort of causal inference being considered. Contrary to general belief, it is shown that neither ‘no confounding’ nor ‘randomization’ are sufficient assumptions for testing the fundamental causal hypothesis of ‘no causation’. They become sufficient, however, if supplemented by a ‘modelling’ assumption for how unobserved covariates affect potential responses. Without supplementary assumptions, no confounding and randomization allow testing of weaker but more important hypotheses, such as ‘no mean effect’. This follows from the sufficiency of an assumption of ‘ignorable treatment assignment’ for testing the hypothesis of ‘no distribution effect‘. It is argued that these ideas will have some operational relevance if we can quantify our beliefs about how closely assumptions hold. to illustrate, a novel form of sensitivity analysis is contrasted with an analysis presented by Rosenbaum and Rubin.
Bayesian Interval Estimates Which are Also Confidence Intervals
Authors: Severini Thomas A.
Published: December 2018

Let Y  1, . . ., Yn denote independent observations each distributed according to a distribution depending on a scalar parameter θ suppose that we are interested in constructing an interval estimate for θ. One approach is to use Bayesian inference. For a given prior density, we can construct an interval such that the posterior probability that θ lies in the interval is some specified value. In this paper, a method is given for constructing a Bayesian interval estimate such that the coverage probability of the interval is approximately equal to the posterior probability of the interval.
Canonical Correlation Analysis When the Data are Curves
Authors: Leurgans S. E. , Moyeed R. A. , Silverman B. W.
Published: December 2018

It is not immediately straightforward to extend canonical correlation analysis to the context of functional data analysis, where the data are themselves curves or functions. The obvious approach breaks down, and it is necessary to use a method involving smoothing in some way. Such a method is introduced and discussed with reference to a data set on human gait. The breakdown of the unsmoothed method is illustrated in a practical context and is demonstrated theoretically. A consistency theorem for the smoothed method is proved.
Adversarial Life Testing
Authors: Lindley Dennis V. , Singpurwalla Nozer D.
Published: December 2018

The life testing of items that exhibit a distribution of times to failure is undertaken for making decisions such as design qualification and reliability demonstration. In such contexts, procedures based on the Bayesian paradigm have assumed a common prior distribution of item reliability by both the consumer and the manufacturer. In this paper we consider the adversarial situation wherein both parties agree on a statistical model for lifetimes but use different prior distributions. We require that the consumer's criteria for acceptance and rejection be known to the manufacturer. We illustrate our approach via the case of exponentially distributed life lengths and relate it to the approach specified in standard MIL STD 781C.
Second-Order Approach to Local Influence
Authors: Wu Xizhi , Luo Zhen
Published: December 2018

To examine global and local influence, and their relations for regression models, we study the perturbation-formed surface of a variable, such as the maximum likelihood estimate of a parameter, by evaluating the second derivative or curvature of the surface. (The corresponding slope of this surface is related to the curvature of the likelihood displacement surface of Cook.) Examples show that this approach, with the aid of plots, is helpful not only to discover influential cases including those hidden in an individual global sense but also to understand the nature of influence.
Approximate Bayesian Inference with the Weighted Likelihood Bootstrap
Authors: Newton Michael A. , Raftery Adrian E.
Published: December 2018

We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a posterior distribution. This method is often easy to implement, requiring only an algorithm for calculating the maximum likelihood estimator, such as iteratively reweighted least squares. In the generic weighting scheme, the WLB is first order correct under quite general conditions. Inaccuracies can be removed by using the WLB as a source of samples in the sampling-importance resampling (SIR) algorithm, which also allows incorporation of particular prior information. The SIR-adjusted WLB can be a competitive alternative to other integration methods in certain models. Asymptotic expansions elucidate the second-order properties of the WLB, which is a generalization of Rubin's Bayesian bootstrap. The calculation of approximate Bayes factors for model comparison is also considered. We note that, given a sample simulated from the posterior distribution, the required marginal likelihood may be simulation consistently estimated by the harmonic mean of the associated likelihood values; a modification of this estimator that avoids instability is also noted. These methods provide simple ways of calculating approximate Bayes factors and posterior model probabilities for a very wide class of models.
The Linear Spatial-Temporal Interaction Process and its Relation to 1/ω-Noise
Authors: Renshaw Eric
Published: December 2018

If we are to gain an understanding of the evolutionary processes which generate pattern then time cannot be ignored, since a purely spatial viewpoint disregards the fundamental generating mechanism of the underlying process. We therefore develop a spectral approach based on a general growth–interaction model; individuals grow at intrinsic rate λ, while two individuals a distance r apart interact at rate ar times their size difference. Special cases considered involve nearest neighbour, geometric, logarithmic and Cauchy-type dependence. A separate study is made of ‘long-term persistence‘; an autoregressive binomial weight structure is used to generate processes with power law spectra ωd. We examine the effect of arranging the general process on a ring of N sites, and then use this structure to simulate nearest neighbour and binomial interaction.
Prequential Level of Impossibility with Some Applications
Authors: Vovk V. G. , V'Yugin V. V.
Published: December 2018

Within Dawid's prequential framework we introduce a quantitative measure of disagreement between a probabilistic theory and observations. Using this measure we reconsider Howard's theorem on Laplace's rule of succession and Dawid's result on the prequential efficiency of the Bayesian rule.
Adjusted Versus Conditional Likelihood: Power Properties and Bartlett-Type Adjustment
Authors: Ghosh J. K. , Mukerjee Rahul
Published: December 2018

Under contiguous alternatives the adjusted likelihood ratio (ALR) test is seen to have the same power properties as the conditional likelihood ratio test up to the third order of comparison. In particular, an optimum property of the ALR test in terms of second-order local maximinity follows. It is also seen that the ALR statistic admits a Bartlett-type adjustment.
Robust Methods for Recursive Autoregressive Moving Average Estimation
Authors: McDougall A. J.
Published: December 2018

In this paper we consider the problem of fitting, recursively in time, an autoregressive moving average (ARMA) model to time series data where outliers may be present. Although many recursive estimation procedures are available for fitting ARMA models, they are based on the recursive least squares algorithm which is known to be badly affected by additive outliers. to minimize the impact of these sort of outliers we investigate some robustified versions of the recursive maximum likelihood procedure. The problem of incorporating robust scale recursion is discussed and asymptotic properties of the procedures are examined. We also present results from simulation studies which compare adaptive versions of these procedures in the non-stationary case.
Fractional Factorial Designs for Factors with a Prime Number of Quantitative Levels
Authors: Edmondson R. N.
Published: December 2018

A technique for single degree of freedom partitioning of canonical contrasts for prime level factorials with quantitative levels is reviewed. The relationship between canonical contrasts and the polynomial contrasts of response function models is discussed. Aliasing of canonical contrasts in fractional factorials is examined and alias sets from principal fractions are shown to have a particularly simple structure. Second-order response function designs built from single principal fractions or from sums of two or four different principal fractions are discussed. The accommodation of nuisance trend effects in experiments with quantitative factor effects is exemplified. Tables of fractional factorial designs for factors with three or five quantitative levels are presented.
Dispersion Effects from Fractional Factorials in Taguchi's Method of Quality Design
Authors: Rosenbaum Paul R.
Published: December 2018

Taguchi proposed direct product designs for studying the influence of control factors on the dispersion introduced by error factors. The current paper presents several general results concerning the use of orthogonal array designs to estimate the effects of control factors on the dispersive effects of error factors. Sufficient conditions are given such that dispersion estimates are ‘undistorted’, in that they estimate the same quantity in fractional factorials as they would in complete factorials. The conditions concern the strength of the orthogonal array and the complexity of the linear model. Taguchi's designs are ‘reliable’ in that they prevent interactions among control factors from being mistaken for dispersion effects involving error effects. There are other reliable orthogonal array designs that are, in some ways, preferable to Taguchi's direct product designs in that they have greater strength or resolution with the same number of observations.
Multiple-Stage Non-Homogeneous Markov Models for the Acquired Immune Deficiency Syndrome Epidemic
Authors: Billard L. , Zhao Zhen
Published: December 2018

Numerous particular models have been introduced in recent years to explain various specific aspects of the underlying mechanism associated with the spread of the acquired immune deficiency syndrome (AIDS) epidemic. Many of these models can be classified as compartmental models. In this work we establish the mathematical framework and provide solutions for the class of compartmental stochastic models for which the transition probabilities between stages of the model are both time and state dependent. We obtain expressions for the state probabilities, and for the mean and variance of the number of AIDS cases as a function of time. to illustrate the theoretical results we compare a four- and five-stage model with the actual number of reported AIDS cases for several populations.
Inverse Problems in Fractal Construction: Hellinger Distance Method
Authors: Chen Jiahua , Kalbfleisch J. D.
Published: December 2018

Some images can be approximated by the geometry of fractals that are attractors of iterative function systems. The inverse problems to be discussed involve searching for an iterative function system that best approximates a given image. Existing methods often assume full knowledge of the image and rely on complicated computations to avoid local optima in the distance measure being used. We propose the use of a revised Hellinger distance in addressing inverse problems. It is shown that the new method does not require full knowledge of the image to be approximated and can eliminate most of the unwanted locally optimal approximations.
Non-Regular Maximum Likelihood Problems
Authors: Cheng R. C. H. , Traylor L.
Published: December 2018

Four non-regular estimation problems are reviewed and discussed. One (the unbounded likelihood problem) involves distributions with infinite spikes, for which maximum likelihood can fail to give consistent estimators. A comparison is made with modified likelihood and spacings methods which do give efficient estimators in this case. An application to the Box–Cox shifted power transform is given. The other three problems occur when the true parameter lies in some special subregion. In one (the constrained parameter problem) the subregion is a boundary. The other two (the embedded model and the indeterminate parameters problems) occur when the model takes on a special form in the subregion. These last two problems have previously been investigated separately. We show that they are equivalent in some situations. Both often arise in non-linear models and we give a directed graph approach which allows for their occurrence in nested model building. It is argued that many non-regular problems can be handled systematically without having to resort to elaborate technical assumptions. Relatively uncomplicated methods may be used provided that the underlying nature of the non-regularity is understood.
Assessment and Propagation of Model Uncertainty
Authors: Draper David
Published: December 2018

In most examples of inference and prediction, the expression of uncertainty about unknown quantities y on the basis of known quantities x is based on a model M that formalizes assumptions about how x and y are related. M will typically have two parts: structural assumptions S, such as the form of the link function and the choice of error distribution in a generalized linear model, and parameters θ whose meaning is specific to a given choice of S. It is common in statistical theory and practice to acknowledge parametric uncertainty about θ given a particular assumed structure S; it is less common to acknowledge structural uncertainty about S itself. A widely used approach involves enlisting the aid of x to specify a plausible single ‘best’ choice S* for S, and then proceeding as if S* were known to be correct. In general this approach fails to assess and propagate structural uncertainty fully and may lead to miscalibrated uncertainty assessments about y given x. When miscalibration occurs it will often result in understatement of inferential or predictive uncertainty about y, leading to inaccurate scientific summaries and overconfident decisions that do not incorporate sufficient hedging against uncertainty. In this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the US space shuttle.
Covariance Structure and Convergence Rate of the Gibbs Sampler with Various Scans
Authors: Liu Jun S. , Wong Wing H. , Kong Augustine
Published: December 2018

This paper presents results on covariance structure and convergence for the Gibbs sampler with both systematic and random scans. It is shown that, under conditions that guarantee the compactness of the Markov forward operator and irreducibility of the corresponding chain, the Gibbs sampling scheme converges geometrically in terms of Pearson χ  2-distance. In particular, for the random scan, the autocovariance can be expressed as variances of iterative conditional expectations. As a consequence, the autocorrelations are all positive and decrease monotonically.
A Bandwidth Selector for Bivariate Kernel Regression
Authors: Herrmann Eva , Wand M. P. , Engel Joachim , Gasser Theo
Published: December 2018

For two and higher dimensional kernel regression, currently available bandwidth selection procedures are based on cross-validation or related penalizing ideas. However, these techniques have been shown to suffer from high sample variability and, in addition, can sometimes be difficult to implement when a vector of bandwidths needs to be selected. In this paper we propose a selector based on an iterative plug-in approach for bivariate kernel regression. It is shown to give satisfactory results and can be quickly computed. Our ideas can be extended to higher dimensions.
Local Likelihood Based on Kernel Censoring
Authors: Copas J. B.
Published: December 2018

By drawing an analogy with likelihood for censored data, a local likelihood function is proposed which gives more weight to observations near a region of interest in the sample space. Resulting methods can be used for assessing local departures from a parametric model, and for semiparametric density estimation. Some theory, and three examples, is given.
Collapsibility of Logistic Regression Coefficients
Authors: Guo Jianhua , Geng Zhi
Published: December 2018

In this paper we discuss collapsibility of logistic regression coefficients over a background variable and present necessary and sufficient conditions for collapsibility.
Statistical Inference for a Two-State Markov Model of a Single Ion Channel, Incorporating Time Interval Omission
Authors: Ball F. G. , Davies S. S.
Published: December 2018

A two-state Markov model of a single ion channel, in which successive open and closed sojourns follow negative exponential distributions with respective means μO and μC, is considered. Estimation of (μO μC) when brief sojourns in the open and closed states fail to be detected is studied. The moment estimating equations for (μO, μC) have no, one or two solutions, with two being the most common situation. The asymptotic behaviour of method-of-moments estimators of (μO, μC), as the number of sojourns becomes large, is determined, and the resulting confidence regions for (μO, μC) are compared with those derived by Clarke, Milne and Yeo under an approximate model. A new method of discriminating between the two solutions of the moment estimating equations is presented. The theory is illustrated by numerical examples taken from ion channel literature.
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Authors: Benjamini Yoav , Hochberg Yosef
Published: December 2018

The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
Regression Models with Incidental Parameters
Authors: Mak Tak K.
Published: December 2018

It is known that iterative weighted least squares (IWLS) does not merely yield a convenient way of fitting the general class of regression models considered by Green but is also a useful means of unifying techniques of model checking. However, an extension of these results to models containing nuisance or incidental parameters has made little progress beyond some special cases. Unfortunately, regression models with nuisance parameters that are not dispersion parameters arise very commonly in applications. The present paper presents a unified treatment of regression models containing incidental parameters that are permitted to contain covariates as well as being functionally dependent on the predictor or regression parameters. For the general regression model considered it is shown that Fisher's scoring algorithm can also be implemented as IWLS. Unlike the commonly suggested fitting strategy which updates the regression parameters with the nuisance parameters assumed known and set at the current values, the present approach yields the correct asymptotic covariance matrix for the regression parameter estimates. By considering simultaneous inference, residuals and influence measures which accommodate the effect of estimating the incidental parameters are outlined.
Multivariate Discriminant Analysis and Maximum Penalized Likelihood Density Estimation
Authors: Granville Vincent , Rasson Jean Paul
Published: December 2018

A new theoretical point of view is discussed in the framework of density estimation. The multivariate true density, viewed as a prior or penalizing factor in a Bayesian framework, is modelled by a Gibbs potential. Estimating the density consists in maximizing the posterior. For efficiency of time, we are interested in an approximate estimator f̂ = Bπ of the true density f, where B is a stochastic operator and π is the raw histogram. Then, we investigate the discrimination problem, introducing an adaptive bandwidth depending on the k nearest neighbours and chosen to optimize the cross-validation criterion. Our final classification algorithm referred to as APML for approximate penalized maximum likelihood compares favourably in terms of error rate and time efficiency with other algorithms tested, including multinormal, nearest neighbour and convex hull classifiers.
Some Tests Based on Extreme Values for a Parametric Survival Model
Authors: Crowder Martin
Published: December 2018

Hougaard proposed a scaled, exponentially tilted, positive stable distribution for the frailty in survival analysis. This leads to a class of survivor functions which encompasses certain special cases. Some tests based on extreme sample values are proposed for assessing the fit of these submodels within a full model. The tests are applied to some data on the breaking strengths of carbon fibres.
The Cost of Adding Parameters to a Model
Authors: Taylor Jeremy M. G. , Siqueira Arminda L. , Weiss Robert E.
Published: December 2018

For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data; the other scenario is where a subset of the parameters is assumed known at their true values and the remaining parameters are estimated. We focus on quantities of interest which are defined on the scale of the response variable. We show that, under certain conditions, the ratio of a weighted sum across the design points of the variance of the quantity of interest is given by q/p, where q and p are the number of free parameters in the two scenarios. Thus, in this average sense, the inflation in variance associated with adding parameters, also interpreted as the cost of adding parameters to a model, is directly proportional to the number of parameters. We study models involving power transformations, non-linear models and exponential family models.
Using Bootstrap Likelihood Ratios in Finite Mixture Models
Authors: Feng Z. D. , McCulloch C. E.
Published: December 2018

Statistical inference using the likelihood ratio statistic for the number of components in a mixture model is complicated when the true number of components is less than that of the proposed model since this represents a non-regular problem: the true parameter is on the boundary of the parameter space and in some cases the true parameter is in a nonidentifiable subset of the parameter space. The maximum likelihood estimator is shown to converge to the subset characterized by the same density function, and connection is made to the bootstrap method proposed by Aitkin and co-workers and McLachlan for testing the number of components in a finite mixture and deriving confidence regions in a finite mixture.
Hierarchical Generalized Linear Models
Authors: Lee Y. , Nelder J. A.
Published: December 2018

We consider hierarchical generalized linear models which allow extra error components in the linear predictors of generalized linear models. The distribution of these components is not restricted to be normal; this allows a broader class of models, which includes generalized linear mixed models. We use a generalization of Henderson's joint likelihood, called a hierarchical or h-likelihood, for inferences from hierarchical generalized linear models. This avoids the integration that is necessary when marginal likelihood is used. Under appropriate conditions maximizing the h-likelihood gives fixed effect estimators that are asymptotically equivalent to those obtained from the use of marginal likelihood; at the same time we obtain the random effect estimates that are asymptotically best unbiased predictors. An adjusted profile h-likelihood is shown to give the required generalization of restricted maximum likelihood for the estimation of dispersion components. A scaled deviance test for the goodness of fit, a model selection criterion for choosing between various dispersion models and a graphical method for checking the distributional assumption of random effects are proposed. The ideas of quasi-likelihood and extended quasi-likelihood are generalized to the new class. We give examples of the Poisson–gamma, binomial–beta and gamma–inverse gamma hierarchical generalized linear models. A resolution is proposed for the apparent difference between population-averaged and subject-specific models. A unified framework is provided for viewing and extending many existing methods.
On Sample Reuse Methods for Dependent Data
Authors: Hall Peter , Jing Bingyi
Published: December 2018

We suggest a sample reuse method for dependent data, based on a cross between the block bootstrap and Richardson extrapolation. Instead of simulating a same size resample by resampling blocks and placing them end to end, it analyses the blocks directly and employs a variant of Richardson extrapolation to adjust for block size. A simple empirical rule, also based on Richardson extrapolation, is suggested for empirically selecting the block size. Performance in the contexts of distribution and bias estimation is discussed via theoretical analysis and numerical simulation.
The impact of apelin and relaxin plasma levels in masked hypertension and white coat hypertension
Authors: Sanidas Elias , Tsakalis Kostas , Papadopoulos Dimitrios P. , Zerva Kanella , Velliou Maria , Perrea Despoina , Mantzourani Marina , Iliopoulos Dimitrios , Barbetseas John
Published: December 2018

Masked hypertension (HTN) and white coat hypertension represent two reverse forms of clinical HTN with questionable prognostic significance. Recent evidence supports that low apelin and relaxin plasma levels contribute to vascular damage accelerating atherogenesis and predisposing to HTN and cardiovascular (CV) events. The aim of this study was to compare apelin and relaxin plasma levels between patients with masked hypertension (MH) and those with white coat HTN (WCH). Overall, 130 patients not receiving antihypertensive therapy were studied. All patients underwent 24‐hour ambulatory BP monitoring (ABPM) and office BP measurements. Plasma apelin and relaxin levels were measured by ELISA method. According to BP recordings, 24 subjects had MH (group A) and 32 had WCH (group B). Apelin (200 ± 111 pg/mL vs 305 ± 127 pg/mL, P < 0.01) and relaxin (35.2 ± 6.7 pg/mL vs 46.8 ± 23.6 pg/mL, P < 0.01) plasma levels were significantly lower in patients with MH compared to those with WCH, respectively. In conclusion, our findings showed that patients with MH had significantly lower apelin and relaxin levels compared to those with WCH. This observation implies an additional prognostic role for adipokines supporting the concept that MH is closer to essential HTN whereas WCH is a more benign condition.
Aminothiazoles inhibit osteoclastogenesis and PGE2 production in LPS‐stimulated co‐cultures of periodontal ligament and RAW 264.7 cells, and RANKL‐mediated osteoclastogenesis and bone resorption in PBMCs
Authors: Kats Anna , Gerasimcik Natalija , Näreoja Tuomas , Nederberg Jonas , Grenlöv Simon , Lagnöhed Ekaterina , Desai Suchita , Andersson Göran , Yucel‐Lindberg Tülay
Published: December 2018

Inflammatory mediator prostaglandin E2 (PGE2) contributes to bone resorption in several inflammatory conditions including periodontitis. The terminal enzyme, microsomal prostaglandin E synthase‐1 (mPGES‐1) regulating PGE2 synthesis is a promising therapeutic target to reduce inflammatory bone loss. The aim of this study was to investigate effects of mPGES‐1 inhibitors, aminothiazoles TH‐848 and TH‐644, on PGE2 production and osteoclastogenesis in co‐cultures of periodontal ligament (PDL) and osteoclast progenitor cells RAW 264.7, stimulated by lipopolysaccharide (LPS), and bone resorption in RANKL‐mediated peripheral blood mononuclear cells (PBMCs). PDL and RAW 264.7 cells were cultured separately or co‐cultured and treated with LPS alone or in combination with aminothiazoles. Multinucleated cells stained positively for tartrate‐resistant acid phosphatase (TRAP) were scored as osteoclast‐like cells. Levels of PGE2, osteoprotegerin (OPG) and interleukin‐6, as well as mRNA expression of mPGES‐1, OPG and RANKL were analysed in PDL cells. PBMCs were treated with RANKL alone or in combination with aminothiazoles. TRAP‐positive multinucleated cells were analysed and bone resorption was measured by the CTX‐I assay. Aminothiazoles reduced LPS‐stimulated osteoclast‐like cell formation both in co‐cultures and in RAW 264.7 cells. Additionally, aminothiazoles inhibited PGE2 production in LPS‐stimulated cultures, but did not affect LPS‐induced mPGES‐1, OPG or RANKL mRNA expression in PDL cells. In PBMCs, inhibitors decreased both osteoclast differentiation and bone resorption. In conclusion, aminothiazoles reduced the formation of osteoclast‐like cells and decreased the production of PGE2 in co‐cultures as well as single‐cell cultures. Furthermore, these compounds inhibited RANKL‐induced bone resorption and differentiation of PBMCs, suggesting these inhibitors for future treatment of inflammatory bone loss such as periodontitis.
Deciphering the SUMO code in the kidney
Authors: Yang Zhen , Zhang Yuming , Sun Shiren
Published: December 2018

SUMOylation of proteins is an important regulatory element in modulating protein function and has been implicated in the pathogenesis of numerous human diseases such as cancers, neurodegenerative diseases, brain injuries, diabetes, and familial dilated cardiomyopathy. Growing evidence has pointed to a significant role of SUMO in kidney diseases such as DN, RCC, nephritis, AKI, hypertonic stress and nephrolithiasis. Recently, emerging studies in podocytes demonstrated that SUMO might have a protective role against podocyte apoptosis. However, the SUMO code responsible for beneficial outcome in the kidney remains to be decrypted. Our recent experiments have revealed that the expression of both SUMO and SUMOylated proteins is appreciably elevated in hypoxia‐induced tubular epithelial cells (TECs) as well as in the unilateral ureteric obstruction (UUO) mouse model, suggesting a role of SUMO in TECs injury and renal fibrosis. In this review, we attempt to decipher the SUMO code in the development of kidney diseases by summarizing the defined function of SUMO and looking forward to the potential role of SUMO in kidney diseases, especially in the pathology of renal fibrosis and CKD, with the goal of developing strategies that maximize correct interpretation in clinical therapy and prognosis.
A Comparison of Constraint Programming and Mixed‐Integer Programming for Automated Test‐Form Generation
Authors: Li Jie , van der Linden Wim J.
Published: December 2018

The final step of the typical process of developing educational and psychological tests is to place the selected test items in a formatted form. The step involves the grouping and ordering of the items to meet a variety of formatting constraints. As this activity tends to be time‐intensive, the use of mixed‐integer programming (MIP) has been proposed to automate it. The goal of this article is to show how constraint programming (CP) can be used as an alternative to automate test‐form generation problems with a large variety of formatting constraints, and how it compares with MIP‐based form generation as for its models, solutions, and running times. Two empirical examples are presented: (i) automated generation of a computerized fixed‐form; and (ii) automated generation of shadow tests for multistage testing. Both examples show that CP works well with feasible solutions and running times likely to be better than that for MIP‐based applications.
A New Person‐Fit Statistic for the Lognormal Model for Response Times
Authors: Sinharay Sandip
Published: December 2018

Response‐time models are of increasing interest in educational and psychological testing. This article focuses on the lognormal model for response times, which is one of the most popular response‐time models, and suggests a simple person‐fit statistic for the model. The distribution of the statistic under the null hypothesis of no misfit is proved to be a χ2 distribution. A simulation study and a real data example demonstrate the usefulness of the suggested statistic.
Gathering and Evaluating Validity Evidence: The Generalized Assessment Alignment Tool
Authors: Cizek Gregory J. , Kosh Audra E. , Toutkoushian Emily K.
Published: December 2018

Alignment is an essential piece of validity evidence for both educational (K‐12) and credentialing (licensure and certification) assessments. In this article, a comprehensive review of commonly used contemporary alignment procedures is provided; some key weaknesses in current alignment approaches are identified; principles for evaluating alignment methods are distilled; and a new approach to investigating alignment is proposed and illustrated. The article concludes with suggestions for alignment research and practice.
Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data
Authors: Shear Benjamin R.
Published: December 2018

When contextual features of test‐taking environments differentially affect item responding for different test takers and these features vary across test administrations, they may cause differential item functioning (DIF) that varies across test administrations. Because many common DIF detection methods ignore potential DIF variance, this article proposes the use of random coefficient hierarchical logistic regression (RC‐HLR) models to test for both uniform DIF and DIF variance simultaneously. A simulation study and real data analysis are used to demonstrate and evaluate the proposed RC‐HLR model. Results show the RC‐HLR model can detect uniform DIF and DIF variance more accurately than standard logistic regression DIF models in terms of bias and Type I error rates.
A New Facets Model for Rater's Centrality/Extremity Response Style
Authors: Jin Kuan‐Yu , Wang Wen‐Chung
Published: December 2018

The Rasch facets model was developed to account for facet data, such as student essays graded by raters, but it accounts for only one kind of rater effect (severity). In practice, raters may exhibit various tendencies such as using middle or extreme scores in their ratings, which is referred to as the rater centrality/extremity response style. To achieve better measurement quality in rater data, it is desirable to simultaneously consider both rater severity and rater centrality/extremity. A new facets model is thus developed by adding to the Rasch facets model a weight parameter for the item thresholds for each rater. The parameters of the new facets model can be estimated with the JAGS freeware. An empirical example is provided to illustrate the implications and applications of the new model. Two simulation studies were conducted. The first simulation was to evaluate the parameter recovery of the new facets model and the consequences of ignoring the effects of rater centrality/extremity on parameter estimation. The second simulation was to illustrate how rater severity affects the relationship between rater centrality and the standard deviation of raw rating scores.