bluuebunny/crossref_metadata_2025 huggingface dataset:
Asymptotically Distribution-Free Simultaneous Confidence Region of Treatment Differences in a Randomized Complete Block Design
Authors: Wei L. J.
Published: December 2018

For a randomized complete block design with additive block effects, an asymptotically distribution-free simultaneous confidence region of pairwise treatment differences is presented. The corresponding confidence bound has an explicit form and is easily obtained. Asymptotic relative efficiency comparisons with other known confidence regions are made to show the advantage of the proposed procedure. The case of treatment against control is also discussed.
On a Structural Property of Finite Distributions
Authors: Panaretos John
Published: December 2018

This note is concerned with the derivation of the distribution of a random variable X in terms of the distribution of Y given X, where X, Y are discrete random variables with finite support.
On the Trace Efficiency for Control of Reinforced Balanced Incomplete Block Designs
Authors: Constantine Gregory M.
Published: December 2018

It is shown that in any block design containing a set of treatments and a control the average variance of the best linear unbiased estimates of the elementary contrasts with the control is proportional to the trace of the inverse of a suitable principal minor of the information matrix. A BIB design with blocks reinforced by the control is then proved to minimize this average variance over all the designs which contain the control once in every block. The result extends to the setting of two-way elimination of heterogeneity when the control appears bk  -1 times, and any other variety appears rk  -1 times, in each row. This latter balance can be achieved whenever a BIB design exists and the numerical conditions allow.
Some Minimum Variance Block Designs for Estimating Treatment Differences
Authors: Jacroux Mike
Published: December 2018

In this paper we consider experimental settings requiring usage of a block design in which v treatments are to be tested in b blocks of size k. A block design is called MV-optimal in such a setting if the maximal variance with which it estimates elementary treatment differences is minimal among all available designs. MV-optimal designs are particularly useful in exploratory experiments where as much information as possible is desired on the effects of all treatments being studied. Some sufficient conditions are derived which can be used to establish the MV-optimality of certain types of generalized group divisible designs as defined in Jacroux (1982) and some easy methods for constructing such MV-optimal designs are given.
Bayesian “Confidence Intervals” for the Cross-Validated Smoothing Spline
Authors: Wahba Grace
Published: December 2018

We consider the model Y(ti)=g(ti) + ∈i, i = 1, 2, …, n, where g(t), t ∈ [0, 1] is a smooth function and the {∈i} are independent N(0, σ2) errors with σ2 unknown. The cross-validated smoothing spline can be used to estimate g non-parametrically from observations on Y(ti), i= 1, 2, …, n, and the purpose of this paper is to study confidence intervals for this estimate. Properties of smoothing splines as Bayes estimates are used to derive confidence intervals based on the posterior covariance function of the estimate. A small Monte Carlo study with the cubic smoothing spline is carried out to suggest by example to what extent the resulting 95 per cent confidence intervals can be expected to cover about 95 per cent of the true (but in practice unknown) values of g(ti), i= 1,2,…,n. The method was also applied to one example of a two-dimensional thin plate smoothing spline. An asymptotic theoretical argument is presented to explain why the method can be expected to work on fixed smooth functions (like those tried), which are “smoother” than the sample functions from the prior distributions on which the confidence interval theory is based.
Recursive Estimation in Non-Linear Time Series Models of Autoregressive Type
Authors: Aase Knut K.
Published: December 2018

An adaptive estimation principle is considered for non-standard time series models; non-linear autoregressive processes in one dimension. Because of the recursive nature of the resulting estimator, it is computationally appealing, especially when a time series is considered as a flow of data. The least squares estimator is, for example, quite likely to be inferior. Asymptotic properties are derived under fairly weak assumptions, and special applications are considered. Monte Carlo simulations are used for illustration.
Improved Likelihood Ratio Statistics for Generalized Linear Models
Authors: Cordeiro Gauss M.
Published: December 2018

This paper gives a general formula for the expected value of the likelihood ratio statistic for generalized linear models, corrected up to terms of order n  -1, where n is the size of the sample. The application of this formula to some models is illustrated.
An Asymptotic Expansion Associated with the Maximum Likelihood Estimators in Arma Models
Authors: Tanaka Katsuto
Published: December 2018

A technique is given for the Edgeworth type asymptotic expansion for the joint as well as marginal and conditional distributions of the maximum likelihood estimators in autoregressive moving-average (ARMA) models. Our methodology is illustrated and results on the expansions for some simple ARMA models are presented.
Asymptotic Results on the Greenwood Statistic and Some of its Generalizations
Authors: Rao J. S. , Kuo Morgan
Published: December 2018

There has been a renewed interest in the Greenwood statistic for goodness of fit and its percentage points. See Burrows (1979), Currie (1981) and Stephens (1981). This statistic, which is based on the sum of squares of the sample spacings, is known to be locally most powerful among all tests based symmetrically on the simple one-step spacings. This paper discusses some generalizations of this statistic based on higher-order or m-step spacings and shows them to be asymptotically more efficient. It is also shown that the limiting efficiency increases further if the test is based on overlapping m-step spacings as opposed to the disjoint (or non-overlapping) m-step spacings.
Testing Misspecification in Vector Time Series Models with Exogenous Variables
Authors: Poskitt D. S. , Tremayne A. R.
Published: December 2018

In this paper we consider the application of hypothesis testing procedures to ARMAX systems. The score, or Lagrangian multiplier, principle is used to obtain mis-specification tests appropriate to these models. The structure of the information or sensitivity matrix is investigated in order to discuss the identifiability of alternative models and is used to show the existence of a set of admissible alternatives that are locally equivalent to one another. This notion is then developed to suggest why the score test is to be regarded as a pure significance test.
Efficient Nonparametric Estimation of Mixture Proportions
Authors: Hall Peter , Titterington D. M.
Published: December 2018

By constructing a sequence of multinomial approximations and related maximum likelihood estimators, we derive a Cramér-Rao lower bound for nonparametric estimators of the mixture proportions and thereby characterize asymptotically optimal estimators. For the case of the sampling model M2 of Hosmer (1973) it is shown that the sequence of maximum likelihood estimators, which can be obtained explicitly, is asymptotically optimal in this sense. The results hold true even when the multinomial approximations involve cells chosen adaptively, from the data, in a well-specified way.
Likelihood Analysis of Spatial Point Patterns
Authors: Ogata Yosihiko , Tanemura Masaharu
Published: December 2018

The likelihood procedure for estimating the pairwise interaction potential function is developed for statistical analysis of homogeneous spatial point patterns. Approximation methods of the normalizing factor of Gibbs canonical distribution are discussed both to estimate a scale parameter and to measure the softness (or hardness) of repulsive interactions. The approximations are useful up to a considerably high density. The validity of our procedure is demonstrated by some computer experiments. Some real data are analysed.
On the Asymptotic Distribution of the Location Linear Discriminant Function
Authors: Vlachonikolis Ioannis G.
Published: December 2018

The location linear discriminant function forms the basis of the optimal Bayes classification rule when the data contain both binary and continuous variables (Krzanowski, 1975). When the unknown parameters are replaced by their sample estimates, the resulting “estimative” rule has an exact distribution which is too complicated for numerical use. We present here an asymptotic expansion of this distribution and study its performance under various assumptions about the parameters.
Spline Smoothing in a Partly Linear Model
Authors: Heckman Nancy E.
Published: December 2018

Suppose that Yi = X'iβ + f(ti) + εi, 1 ≤ i ≤ n, where β, f, and εi are unknown, but the m-th derivative of f is square integrable. Estimates of β and f are given which minimize the sum of the residual sum of squares and a roughness penalty. It is shown that these estimates are Bayes under a diffuse prior on β and f, and that, under mild conditions on Xi, ti, εi, and the roughness penalty, the estimate of β is consistent and asymptotically normal.
The Analysis of Lattice Square Designs
Authors: Williams E. R. , Ratcliff D. , Ewijk P. H.
Published: December 2018

This paper presents a general definition for lattice square designs and gives a unified approach to the analysis of the class of restricted lattice square designs. This class includes as special cases all the lattice square designs covered in the books by Cochran and Cox (1957) and Federer (1955)
Two-Sample Problem with Bivariate Exchangeable Observations
Authors: Wei L. J.
Published: December 2018

In comparing two groups of subjects, suppose that each subject has two observations which are probabilistically exchangeable. Univariate rank tests which utilize this symmetry property are proposed for testing the equality of the two groups. Efficiencies of these tests are discussed. Point and interval estimators of the location shift parameter are obtained through the proposed rank tests. Examples are also provided to illustrate the proposed procedures.
Some Prediction Properties of Balanced Half-Sample Variance Estimators in Single-Stage Sampling
Authors: Valliant Richard
Published: December 2018

The balanced half-sample (BHS) method of variance estimation is a widely used technique in complex sample surveys. Large-sample prediction properties of the BHS method are given here for the separate ratio and regression estimators. Results are obtained when a large number of units are sampled and divided into two groups within each stratum. An empirical study examines the conditional and unconditional performance of the BHS method when estimating mean squared errors and constructing confidence intervals. The study also includes comparisons with the jackknife and the linearization estimators.
Identification of Nonzero Elements in the Polynomial Matrices of Mixed Varma Processes
Authors: Koreisha S. G. , Pukkila T. M.
Published: December 2018

The parameter density is the polynomial matrices of vector autoregressive moving average (VARMA) time series models is generally very low. Thus, estimation of fully parameterized high order models or of models with many variables is an ineffective, if not impractical, way of utilizing computing power. In the article we present methods for identifying the nonzero elements in VARMA(1,1) models, and show how these procedures can be used to construct models of higher order.
Affine Invariant Rank Methods in the Bivariate Location Model
Authors: Brown B. M. , Hettmansperger T. P.
Published: December 2018

The generalized multivariate median of Oja (Statist. and Prob. Letters  1(1983), 327–332) is used to define a multivariate notion of quantile, or rank, and to define a measure of scatter of multivariate linear models. The latter, when applied to the one- and two-sample bivariate location models, yields affine invariant analogues of the Wilcoxon rank-sum and signed-rank tests, and of the corresponding estimates.
Minimum Contrast Estimation for Spectral Densities of Stationary Processes
Authors: Taniguchi Masanobu
Published: December 2018

Let {z(n)} be a stationary process with mean zero and true spectral density g(λ). In this paper we approximate g(λ) by a parametric family f  θ(λ) and propose a minimum contrast estimator θ^ which minimizes D(fθ,g^N)=∫−ππK{fθ(λ)/g^N(λ)}dλ with respect to θ, where K(.) is an appropriate function and ĝ N(λ) is a nonparametric spectral estimator of g(λ). We give the asymptotic distribution of θ^ and sufficient conditions such that θ^ is asymptotically efficient. Also, we show that for various spectra f  θ(λ), if we choose K(.) in D(f  θ, g) appropriately, we obtain a non-iterative efficient estimator of θ in explicit form.
Estimation and Model Identification for Continuous Spatial Processes
Authors: Vecchia A. V.
Published: December 2018

Formal parameter estimation and model identification procedures for continuous domain spatial processes are introduced. The processes are assumed to be adequately described by a linear model with residuals that follow a second-order stationary Gaussian random field and data are assumed to consist of noisy observations of the process at arbitrary sampling locations. A general class of two-dimensional rational spectral density functions with elliptic contours is used to model the spatial covariance function. An iterative estimation procedure alleviates many of the computational difficulties of conventional maximum likelihood estimation for non-lattice data. The procedure is applied to several generated data sets and to an actual ground-water data set.
A Method for the Estimation and Identification of Transfer Function Models
Authors: Poskitt D. S.
Published: December 2018

This paper proposes an approach to the simultaneous identification and estimation of transfer function models. An estimation procedure is advanced consisting of a sequence of four ordinary least squares calculations and the asymptotic properties of each step are presented, consistency and asymptotic efficiency being shown. A strategy for the identification of the model structure based on a selection criterion that arises naturally from the last step of the estimation algorithm is also investigated. Consideration is given to the numerical implementation of the techniques discussed, and finally a practical example is employed to illustrate the methods.
Arma Models with Double-Exponentially Distributed Noise
Authors: Damsleth Eivind , El-Shaarawi A. H.
Published: December 2018

The marginal and bivariate distributions of the observations generated from a standard autoregressive moving average scheme are derived, assuming the noise to have a double-exponential (Laplace) distribution. The distributions may differ substantially from their Gaussian counterparts. The AR(1) model with double-exponential noise is applied to a series of weekly measurements of sulphate concentration and is shown to give a significantly better fit when compared with the Gaussian model.
A Counter-Example Against the Likelihood Principle
Authors: Joshi V. M.
Published: December 2018

A counter-example against the likelihood principle is strengthened and shown to hold also for a finite sample space.
Bandwidth Choice for Density Derivatives
Authors: Härdle Wolfgang , Marron J. S. , Wand M. P.
Published: December 2018

An adaptation of least squares cross-validation is proposed for bandwidth choice in the kernel estimation of the derivatives of a probability density. The practicality of the method is demonstrated by an example and a simulation study. Theoretical justification is provided by an asymptotic optimality result.
Bayes Estimation for the Marshall–Olkin Exponential Distribution
Authors: Peña Edsel A. , Gupta Arjun K.
Published: December 2018

Bayes estimators of the parameters of the Marshall–Olkin exponential distribution are obtained when random samples from series and parallel systems are available. The estimators are with respect to the quadratic loss function, and the prior distribution allows for prior dependence among the components of the parameter vector. Exact and approximate highest posterior density credible ellipsoids for the parameters are also obtained. In contrast with series sampling, the Bayes estimators under parallel sampling are not in closed form, and numerical procedures are required to obtain estimates. Bayes estimators of the reliability functions are also given. The gain in asymptotic precision of parallel estimates over series estimates is also ascertained theoretically.
Models for Exceedances Over High Thresholds
Authors: Davison A. C. , Smith R. L.
Published: December 2018

We discuss the analysis of the extremes of data by modelling the sizes and occurrence of exceedances over high thresholds. The natural distribution for such exceedances, the generalized Pareto distribution, is described and its properties elucidated. Estimation and model-checking procedures for univariate and regression data are developed, and the influence of and information contained in the most extreme observations in a sample are studied. Models for seasonality and serial dependence in the point process of exceedances are described. Sets of data on river flows and wave heights are discussed, and an application to the siting of nuclear installations is described.
Local Minima in Cross-Validation Functions
Authors: Hall Peter , Marron J. S.
Published: December 2018

The method of least squares cross-validation for choosing the bandwidth of a kernel density estimator has been the object of considerable research, through both theoretical analysis and simulation studies. The method involves the minimization of a certain function of the bandwidth. One of the less attractive features of this method, which has been observed in simulation studies but has not previously been understood theoretically, is that rather often the cross-validation function has multiple local minima. The theoretical results of this paper provide an explanation and quantification of this empirical observation, through modelling the cross-validation function as a Gaussian stochastic process. Asymptotic analysis reveals that the degree of wiggliness of the cross-validation function depends on the underlying density through a fairly simple functional, but dependence on the kernel function is much more complicated. A simulation study explores the extent to which the asymptotic analysis describes the actual situation. Our techniques may also be used to obtain other related results—e.g. to show that spurious local minima of the cross-validation function are more likely to occur at too small values of the bandwidth, rather than at too large values.
Modelling Extreme Multivariate Events
Authors: Coles Stuart G. , Tawn Jonathan A.
Published: December 2018

The classical treatment of multivariate extreme values is through componentwise ordering, though in practice most interest is in actual extreme events. Here the point process of observations which are extreme in at least one component is considered. Parametric models for the dependence between components must satisfy certain constraints. Two new techniques for generating such models are presented. Aspects of the statistical estimation of the resulting models are discussed and are illustrated with an application to oceanographic data.
Bayesian Inference for Medical Screening Tests: Approximations Useful for the Analysis of Acquired Immune Deficiency Syndrome
Authors: Johnson Wesley O. , Gastwirth Joseph L.
Published: December 2018

In this paper, we develop large sample Bayesian methods for assessing the accuracy of screening tests that are used to detect antibodies to the human immunodeficiency virus in donated blood, and for assessing the prevalence of the disease in the population sampled from. We obtain approximate joint and marginal posterior distributions for the predictive values positive and negative of a test and, additionally, we obtain approximate predictive distributions for the number of future individuals that will test positively or be truly positive out of a new sample or population of interest. We illustrate our methods with data from Canada and the UK.
A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation
Authors: Sheather S. J. , Jones M. C.
Published: December 2018

We present a new method for data-based selection of the bandwidth in kernel density estimation which has excellent properties. It improves on a recent procedure of Park and Marron (which itself is a good method) in various ways. First, the new method has superior theoretical performance; second, it also has a computational advantage; third, the new method has reliably good performance for smooth densities in simulations, performance that is second to none in the existing literature. These methods are based on choosing the bandwidth to (approximately) minimize good quality estimates of the mean integrated squared error. The key to the success of the current procedure is the reintroduction of a non-stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.
Diagnostics in Categorical Data Analysis
Authors: Andersen Erling B.
Published: December 2018

Diagnostics as measures of model deviations and of the influence of particular data sets are used extensively in modern regression analysis. For contingency tables, and more generally for the parametric multinomial distribution, it is not the influence of individual observations which is of interest, but rather the contribution to a lack of model fit or to the values of the parameter estimates from a single cell in the table, which must be evaluated. Hence diagnostics for contingency tables take somewhat different forms. In this paper such diagnostics are discussed and their applicability is illustrated with data sets analysed by the Goodman RC association model.
A Nonparametric Estimation Procedure for a Periodically Observed Three-State Markov Process, with Application to Aids
Authors: Frydman Halina
Published: December 2018

Estimation in a three-state Markov process with irreversible transitions in the presence of interval-censored data is considered. A nonparametric maximum likelihood procedure for the estimation of the cumulative transition intensities is presented. A self-consistent estimator of the parameters is defined and it is shown that the maximum likelihood estimator is a self-consistent estimator. This extends the idea of self-consistency introduced by Efron to the estimation of more than one parameter. An algorithm, based on self-consistency equations, is provided for the computation of the estimators. This algorithm is a generalization of an algorithm by Turnbull which yields an estimator of a distribution function for interval-censored univariate data. The methods are applied to Aids data.
On the Removal of Skewness by Transformation
Authors: Hall Peter
Published: December 2018

We suggest empirical transformations for removing most of the skewness of an asymmetric statistic. These transformations may be used as the basis for accurate confidence procedures or hypothesis tests and can be employed in conjunction with either a normal approximation or the bootstrap. Our approach differs from those of other researchers in that the transformation is monotone and invertible.
Consistent Estimation in the Presence of Bandwidth Limitation
Authors: Dabrowski AndrÉ Robert , McDonald David
Published: December 2018

Ion channels are molecular structures embedded in cell membranes which selectively allow ions to cross the membranes. The electrical activity of ion channels has been successfully described by a finite state space semi-Markov process. In this model, some of these states are closed and do not permit any ions to flow through the channel. The remaining states are open and permit ions to cross with a fixed conductance, producing a fixed measurable current across the cell membrane. If the patch of cell membrane under study contains several channels, only the aggregate amount of current crossing the membrane is observable. Several estimation and testing procedures have been developed which estimate the model parameters based on the transitions of this output current process. A significant difficulty in the analysis of such a record is bandwidth limitation, or time interval omission. Events of short duration, such as a channel opening shortly followed by a channel closing, are missed by the recording machinery. We compute a first-order correction term to the count of current level transitions when the current record is bandwidth limited. For small bandwidths, this leads to improved estimates for the mean open time, the mean closed time and the probability that a channel is open at an arbitrary time. We introduce a more general correction process valid for larger bandwidths as well. We apply our techniques to simulated ion channel data and to patch clamp data from pond snails.
Bayesian Intervals with Good Frequentist Behaviour in the Presence of Nuisance Parameters
Authors: Nicolaou Anna
Published: December 2018

Given a random sample from a distribution with density function that depends on an unknown parameter θ = (θ1 . . ., θp), we are concerned with the problem of setting confidence intervals for a particular component of it, say θ1, treating the remaining components as a nuisance parameter. Adopting an objective Bayesian approach, we show that the Bayes intervals with a certain conditional prior density on the parameter of interest, θ1, are confidence intervals as well, having nearly the correct frequency of coverage. The frequentist performance of the proposed intervals is tested in a simulation study for gamma mean and shape parameters.
On Outlier Detection in Time Series
Authors: Ljung Greta M.
Published: December 2018

The estimation and detection of outliers in a time series generated by a Gaussian auto-regressive moving average process is considered. It is shown that the estimation of additive outliers is directly related to the estimation of missing or deleted observations. A recursive procedure for computing the estimates is given. Likelihood ratio and score criteria for detecting additive outliers are examined and are shown to be closely related to the leave-k-out diagnostics studied by Bruce and Martin. The procedures are contrasted with those appropriate for innovational outliers.
From Image Deblurring to Optimal Investments: Maximum Likelihood Solutions for Positive Linear Inverse Problems
Authors: Vardi Y. , Lee D.
Published: December 2018

The problem of recovering an input signal from a blurred output, in an input-output system with linear distortion, is ubiquitous in science and technology. When the blurred output is not degraded by statistical noise the problem is entirely deterministic and amounts to a mathematical inversion of a linear system with positive parameters, subject to positivity constraints on the solution. We show that all such linear inverse problems with positivity restrictions (LININPOS problems for short) can be interpreted as statistical estimation problems from incomplete data based on infinitely large ‘samples’, and that maximum likelihood (ML) estimation and the EM algorithm provide a straightforward method of solution for such problems. This applies to such classical problems as algebraic systems of linear equations, Fredholm's integral equations of the first kind, mixture models, deconvolutions, etc., all with positivity restrictions but with no stochastic components. In connecting the class of LININPOS problems with the corresponding class of ML estimation problems from incomplete data, we unify numerous examples from diverse areas of applications where the EM algorithm has been independently derived over the years, and we present some new opportunities for problems that have previously been unattempted. Examples include signal recovery problems, tomographic reconstructions and optimal investment portfolios, as well as more traditional statistical problems like ML estimation from censored and grouped data, and from incomplete contingency tables. The problem of restoring a ‘true’ image from its linearly distorted version is important in image analysis. Such problems can occur, for example, as a result of lens distortion (e.g. the Hubble space telescope), out-of-focus blur and blur due to relative motion between the subject and the camera during the photographic exposure. These problems often fall in the category of LININPOS problems, in which case the methodology is applicable. We demonstrate it on the problem of deblurring motion.
Continuum Regression and Ridge Regression
Authors: Sundberg Rolf
Published: December 2018

We demonstrate the close relationship between first-factor continuum regression and standard ridge regression. The difference is that continuum regression inserts a scalar compensation factor for that part of the shrinkage in ridge regression that has no connection with tendencies towards collinearity. We interpret this to mean that first-factor continuum regression is preferable in principle to ridge regression if we want protection against near collinearity but do not admit shrinkage as a general principle. Furthermore, our experience indicates that with first-factor continuum regression we can obtain predictors that are at least as mean-squared error efficient as with ridge regression but with less sensitivity to the choice of ridge constant. The scalar compensation factor is easily calculated by just an additional simple linear regression with the ridge regression predictor as regressor.
Characterizing Surface Smoothness Via Estimation of Effective Fractal Dimension
Authors: Constantine A. G. , Hall Peter
Published: December 2018

The fractal dimension D of stationary Gaussian surfaces may be expressed very simply in terms of behaviour of the covariance function near the origin. Indeed, only the covariance of line transect samples is required, and that fact makes practical estimation of D relatively straightforward. The case of non-Gaussian surfaces is more poorly understood, but we might define ‘effective fractal dimension’ in terms of the covariance function, as though the surface were Gaussian. In the present paper we suggest simple practical methods for estimating effective fractal dimension, based on the variogram. Like techniques proposed recently by Taylor and Taylor, ours are founded on log-linear regression. However, the context of our problem is more clearly defined, and so we can develop significantly more detailed theory than Taylor and Taylor could. The problem of the choice of smoothing parameter is addressed, and our methods are applied to real data on the smoothness of a polished metal surface.
Empirical Saddlepoint Approximations for Multivariate M-Estimators
Authors: Ronchetti Elvezio , Welsh A. H.
Published: December 2018

In this paper, we investigate the use of the empirical distribution function in place of the underlying distribution function F to construct an empirical saddlepoint approximation to the density fn of a general multivariate M-estimator. We obtain an explicit form for the error term in the approximation, investigate the effect of renormalizing the estimator, carry out some numerical comparisons and discuss the regression problem.
Laplace Approximation of High Dimensional Integrals
Authors: Shun Zhenming , McCullagh Peter
Published: December 2018

It is shown that the usual Laplace approximation is not a valid asymptotic approximation when the dimension of the integral is comparable with the limiting parameter n. The formal Laplace expansion for multidimensional integrals is given and used to construct asymptotic approximations for high dimensional integrals. One example is considered in which the dimension of the integral is O(n  1/2) and the relative error of the unmodified Laplace approximation is O(1). Nevertheless, it is possible to construct a valid asymptotic expansion by regrouping terms in the formal expansion according to asymptotic order in n.
Spectral Decomposition of the Covariance Matrix of a Multinomial
Authors: Watson Geoffrey S.
Published: December 2018

This paper gives information about the spectral decomposition of the covariance matrices of a multinomial. It is a note to the paper of Tanabe and Sagae which gives fascinating information about the Cholesky decomposition of the covariance matrices of multinomial distributions.
Modelling Extremes of the Areal Rainfall Process
Authors: Coles Stuart G. , Tawn Jonathan A.
Published: December 2018

Risk assessment for many hydrological structures requires an estimate of the extremal behaviour of the rainfall regime within a specified catchment region. In most cases it is the spatially aggregated rainfall which is the key process, though in practice only pointwise rainfall measurements from a network of sites over the region are available. In this paper we address the problem of making inferences about the extremal properties of the aggregated process from the pointwise data. Working within the usual extreme value paradigm, a model is derived in which the resulting distribution is determined by the marginal tail behaviour and spatial dependence at extreme levels of the process. Data collected from a region in the south-west of England are used to illustrate the procedure.
On the Choice of Smoothing Parameter, Threshold and Truncation in Nonparametric Regression by Non-Linear Wavelet Methods
Authors: Hall Peter , Patil Prakash
Published: December 2018

Concise asymptotic theory is developed for non-linear wavelet estimators of regression means, in the context of general error distributions, general designs, general normalizations in the case of stochastic design, and non-structural assumptions about the mean. The influence of the tail weight of the error distribution is addressed in the setting of choosing threshold and truncation parameters. Mainly, the tail weight is described in an extremely simple way, by a moment condition; previous work on this topic has generally imposed the much more stringent assumption that the error distribution be normal. Different approaches to correction for stochastic design are suggested. These include conventional kernel estimation of the design density, in which case the interaction between the smoothing parameters of the non-linear wavelet estimator and the linear kernel method is described.
A New Graphical Tool to Detect Non-Normality
Authors: Ghosh Sucharita
Published: December 2018

A new graphical tool for checking normality of independently and identically distributed observations based on the third derivative T  3 of the logarithm of the empirical moment-generating function is proposed. A significant departure of this function from the horizontal zero line is indicative of non-normality. Behaviour in the neighbourhood of 0 indicates the type of departure from the normal distribution. Examples show that the T  3-method, when used in conjunction with the classical probability plot, can provide valuable insight into the data and be a powerful graphical tool.
Additive Extensions to Generalized Estimating Equation Methods
Authors: Wild C. J. , Yee T. W.
Published: December 2018

The methods underlying vector generalized additive models are extended to provide additive extensions to the generalized estimating equations approaches to multivariate regression problems of Liang and Zeger and the subsequent literature. The methods are illustrated by two examples using correlated binary data concerning the presence or absence of several plant species at the same geographical site and chest pain in male workers from a workforce study. With minor modification, the extensions are shown to apply to longitudinal data.
Counting Process Models for Infectious Disease Data: Distinguishing Exposure to Infection from Susceptibility
Authors: Rhodes Philip H. , Halloran M. Elizabeth , Longini Ira M.
Published: December 2018

Differences in infection rates among types of individuals within a population can arise from differences in amount of exposure to infection or from differences in susceptibility to infection. We derive models for infection rates that incorporate contact rates between individuals and variables affecting susceptibility to infection. We emphasize the distinction between controlling for exposure opportunity (expected exposure) and actual exposure. We present a marked counting process model for the combined contact and infection transmission processes. When the contact process is not observable, we develop thinned counting process models that reduce to a proportional hazards model. We show that the different commonly used parameters for evaluating covariate effects, such as vaccine efficacy, form a hierarchy depending on the amount of information available about the components of the transmission system.
Steps to Parameter Redundancy in Age-Dependent Recovery Models
Authors: Catchpole E. A. , Freeman S. N. , Morgan B. J. T.
Published: December 2018

Recovery data result when marked animals are found dead and their marks are reported. Large banks of such data exist for many species of birds ringed as young. It is often important to include in stochastic models for such data a degree of age variation in both the annual survival and the reporting probabilities. We show how to determine by inspection whether such models are parameter redundant, which identifies the cause of the parameter redundancy when it occurs. We also show how the results extend to a useful example of a model incorporating both time- and age-dependent probabilities.
Delusions and Personal Autonomy
Authors: Ayob Gloria Sibson
Published: December 2018

This article will examine the claim that personal autonomy is impaired by a paradigmatic instance of serious psychopathology – namely, the condition of being delusional – in light of the hierarchical conception of personal autonomy. This conception of personal autonomy aims at yielding value‐neutral judgements about freedom and self‐governance. I will argue that when viewed from the perspective of this specific conception of autonomy, delusions do not necessarily impair an agent's personal autonomy. In order to establish this claim, I will probe the general idea that delusional subjects are beset by a mental disease that is rationally incapacitating, to which the hierarchical theorist might appeal. I argue that, understood within the parameters set by the commitment to value neutrality, this idea fails to provide support for the claim that delusion necessarily impairs personal autonomy. One contribution this article makes to the effort of understanding how delusion impairs personal autonomy is to help us pinpoint the ways in which our value commitments inform our judgements of impaired personal autonomy in delusional agents.
Thermal treatment for soybean flour processing with high‐quality color and reduced Kunitz trypsin inhibitor
Authors: Dahmer Alice Maria , Rigo Aline Andressa , Steffens Juliana , Steffens Clarice , Carrão‐Panizzi Mercedes Concordia
Published: December 2018

In this work thermal treatment was applied in whole soybean flour and bleached whole soybean flour production aimed at Kunitz trypsin inhibitor (KTI) reduction and color. A 22 Central Composite Rotational Design was developed to evaluate the effects of temperature (91.80–148.20 °C) and time (15.9–44.10 min). In general, the bleached whole soybean flour presented higher KTI reduction and better color than whole soybean flour in the evaluated temperature and time ranges. It was observed that at 148.2 °C for 30 min the whole soybean flour presented a lower KTI value (1.56 mg/g) than bleached whole soybean flour (1.48 mg/g) and good luminosity. As the bleached presents an extra step in the treatment process, new experiments were made with whole soybean flour without bleached to evaluate KTI and color reduction as a time function (10–30 min) at 148.2 °C. The time was evaluated the verify the behavior of KTI and color of whole soybean flour. The treatment at 148.2 °C for 20 and 30 min presented the lowest KTI values, however, the L* color value at 20 min was higher and soybean flour was whiter, hence 20 min was considered the best condition for whole soybean flour production.

Practical applications

In order to increase the nutritional quality of soybeans and use them as food, the inactivation of protease inhibitors is required. As soybean nutritional value is adversely affected by protease inhibitors presence that are considered anti‐nutritional factors, these need to be eliminated. Thermal treatment has been used as a physical method to inactivate these factors. However, it must be controlled to avoid essential amino acids destruction. Thermal treatment prior to soybean processing for flour production is common. However, recommendation for the best time and temperature for the grains is nonexistent in order to inactivate and/or reduce their phytochemicals with anti‐nutritional action, to the detriment of those with functional effect, preserving their nutrients. Usually, commercial soybean products are thermally treated with less than 20% of in natura soybean trypsin inhibitory activity.
Delivery of Preventive Oral Health Services by Rurality: A Cross‐Sectional Analysis
Authors: Geiger Caroline K. , Kranz Ashley M. , Dick Andrew W. , Duffy Erin , Sorbero Mark , Stein Bradley D.
Published: December 2018

Purpose

Integrating oral health care into primary care has been promoted as a strategy to increase delivery of preventive oral health services (POHS) to young children, particularly in rural areas where few dentists practice. Using a multistate sample of Medicaid claims, we examined a child's odds of receiving POHS in a medical office by county rurality.

Methods

We used 2012‐2014 Medicaid Analytic extract claims data for 6,275,456 children younger than 6 years in 39 states that allowed Medicaid payment for POHS in medical offices. We used county‐level characteristics from the Area Health Resources Files, including a 3‐level measure of county rurality. We used logistic regression to estimate a child's odds of receiving POHS in a medical office by county rurality, while controlling for other patient and county characteristics.

Findings

POHS in medical offices were received by 7.8% of children. Rates of POHS in medical offices were higher in metropolitan (metro) counties (8.4%) than nonmetro adjacent to metro (5.8%) and nonmetro not adjacent to metro (4.3%). In adjusted analysis, children living in nonmetro not adjacent to metro (OR = 0.79, 95% CI: 0.64‐0.99) and adjacent to metro counties (OR = 0.70, 95% CI: 0.59‐0.82) were significantly less likely to receive POHS in medical offices than children living in metro counties.

Conclusions

In this study of POHS in medical offices among young Medicaid‐enrolled children, we found POHS rates were lowest in nonmetro counties. Given barriers to dental care in rural areas, states should take additional steps beyond allowing Medicaid reimbursement to increase delivery of POHS in medical offices.
Alienation in a four factor world
Authors: Silver Daniel
Published: December 2018

This paper aims to reconstruct the concept of alienation as a live topic for active social theorizing. Joining Marxian and Simmelian ideas, it provides a multi‐dimensional, formal, and synthetic theory of alienation. The paper develops a set of theoretical tools for articulating formal elements of action that make alienation possible, without giving conceptual priority to alienation in the sphere of production, or within that sphere to the alienation of labor. These tools make it possible to derive classical notions of alienation as specific, contingent combinations of multiple elements, theorizing them as concrete socio‐historical configurations of a broader universe of possibilities. They also organize systematic reflection on various forms and relations of alienation; not only those between for instance labor and capital, but also among all four factors of production: land, labor, capital, and entrepreneurship. The paper accordingly develops an original, multi‐dimensional theorization of alienation for a complex, pluralistic world.
Effectiveness and safety of switching to entecavir hepatitis B patients developing kidney dysfunction during tenofovir
Authors: Viganò Mauro , Loglio Alessandro , Labanca Sara , Zaltron Serena , Castelli Francesco , Andreone Pietro , Messina Vincenzo , Ganga Roberto , Coppola Nicola , Marrone Aldo , Russello Maurizio , Marzano Alfredo , Tucci Alessandra , Taliani Gloria , Fasano Massimo , Fagiuoli Stefano , Villa Erica , Bronte Fabrizio , Santantonio Teresa , Brancaccio Giuseppina , Occhipinti Vincenzo , Facchetti Floriana , Grossi Glenda , Rumi Mariagrazia , Lampertico Pietro
Published: December 2018

Background and Aims

Tenofovir disoproxil fumarate (TDF) is recommended for chronic hepatitis B (CHB) treatment, but it may induce kidney dysfunction whose management is not yet known. This Italian, multicentre, retrospective study aimed to assess the efficacy and safety of switching to entecavir (ETV) patients who developed TDF‐associated glomerular and/or tubular dysfunction.

Methods

A total of 103 TDF‐treated patients were included as follows: age 64 years, 83% male, 49% cirrhotics, 98% with undetectable HBV DNA, 47% with previous lamivudine resistance (LMV‐R) and 71% previously treated with adefovir. Twenty‐nine (28%) were switched to ETV because estimated glomerular filtration rate (eGFRMDRD) was <60 mL/min, 37 (36%) because blood phosphate (P) levels were <2.5 mg/dL and 37 (36%) for both reasons. Kidney, liver and virological parameters were recorded every 4 months thereafter.

Results

During 46 (4‐115) months of ETV treatment, all patients’ renal parameters significantly improved as follows: creatinine from 1.30 to 1.10 mg/dL (P < 0.0001), eGFRMDRD from 54 to 65 mL/min (P = 0.002), P from 2.2 to 2.6 mg/dL (P < 0.0001) and maximal tubule phosphate reabsorption (TmPO4/eGFR) from 0.47 to 0.62 mmol/L (P < 0.0001). Thirteen patients (52%) improved their eGFRMDRD class, P levels were normalised in 13 (35%), and eight (22%) showed improvements in both parameters. Viral suppression was maintained in all but five patients (5%), all of whom had been LMV‐R. The 5‐year cumulative probability of ETV‐R was 0% in LMV‐naïve patients, and 11% in LMV‐R patients (P = 0.018).

Conclusions

Entecavir is an effective and safe rescue strategy for CHB patients who develop renal dysfunction during long‐term TDF treatment.
Twelve months of oral ulceration, weight loss and unexplained fevers: a clinicopathologic conference
Authors: O'Sullivan L. , Galvin S. , Flavin R. , Fadalla K. , Healy C.M. , Feeney E.
Published: December 2018

Extranodal NK/T‐cell lymphoma (ENKL) is a rare, aggressive form of non‐Hodgkin's lymphoma with a distinct ethnic predilection favouring populations from Asia and Central and South America. The nasopharynx is the most commonly affected anatomical site. One of the key markers of this disease entity is infection with Epstein‐Barr virus (EBV), which is detected by in situ hybridisation. Histopathology and immunohistochemistry play a vital role in the diagnostic process, with cells typically expressing CD56, TIA‐1 and CD3ε. We present here a very unusual case of ENKL in a Caucasian female with an extranasal presentation, absence of CD56 expression and presence of EBV‐positive cells in the bone marrow. The unusual presentation of the disease in this case, as well as the many diagnostic challenges encountered, are detailed in this paper. It is hoped that this case will prompt clinicians to consider the possibility of an underlying malignant process in cases of refractory oral ulceration with concurrent clinical signs and symptoms.