bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solving Non-Linear Estimation Equations
Authors: Mak Tak K. 
Published: December 2018

SUMMARY
In this paper we consider the numerical computation of a vector parameter estimate θ^ which is a root of a system of unbiased non-linear estimation equations. A sequence {θ (r)} is constructed which converges with a probability approaching 1 to θ^ from any starting value θ (0). Unlike Newton-type algorithms, the θ (r) do not involve matrices of partial derivatives. Furthermore it can be shown that θ(r)−θ^ is of the order Op(n – r/2) so that great accuracy is achieved in very few iterations. When the θ (r) do not have closed forms, an approximation is suggested. An alternative and usually simpler way is also found for deriving analytically the expected value of the matrix of partial derivatives in the asymptotic covariance matrix of θ^. The efficiency of the proposed algorithm is demonstrated numerically and compared with that of the EM and the Newton–Raphson algorithms. Applications to some well-known statistical problems, including missing data analysis, variance component models, familial correlations and errors-in-variables models are also briefly discussed.
Adjusted Versions of Profile Likelihood and Directed Likelihood, and Extended Likelihood
Authors: Barndorff-Nielsen O. E. 
Published: December 2018

SUMMARY
Several adjusted and invariant versions of profile likelihood and of directed likelihood (short for signed log-likelihood ratio) are discussed. These adjusted versions, which fall into two groups, are closely related to modified profile likelihood and modified directed likelihood. The first group has the advantage of not requiring an explicit specification of an ancillary, but partly at a cost of a loss of asymptotic accuracy in comparison with the modified versions. The second group consists of some asymptotically sharpened versions of modified profile likelihood. Furthermore, a concept of extended likelihood is proposed, which associates a likelihood-like function to certain combinants. At the end, the relationship between adjusted profile likelihoods on the one hand and adjusted directed likelihoods on the other is discussed briefly, and in this context a close connection is established between modified profile likelihood and the extended likelihood determined by the modified directed likelihood.
Parameterization of Continuous Time Autoregressive Models for Irregularly Sampled Time Series Data
Authors: Belcher J. , Hampton J. S. , Wilson G. Tunnicliffe 
Published: December 2018

SUMMARY
An increasingly valuable tool for modelling irregularly sampled time series data is the continuous time autoregressive model. The natural parameters in this model are the coefficients of the linear stochastic differential equation describing the process which gives rise to the data. A transformation of these parameters is introduced, based on the Cayley–Hamilton transformation. The new parameter space is identical with that of discrete time autoregressive models. The model is also modified by the introduction of prescribed moving average terms. The resulting modelling improvements include rapid and reliable convergence of parameter estimates and the ability to select the model order by testing whether the highest order coefficient is 0. A geophysical and a medical application illustrate the detection of periodicities in data by using the spectrum of the fitted model.
Affine Invariant Multivariate Multisample Sign Tests
Authors: Hettmansperger Thomas P. , Oja Hannu 
Published: December 2018

SUMMARY
Invariant sign-type tests for the multivariate, multisample location models are developed. The definition of sign is based on the Oja criterion function. Emphasis is on the two-sample location model and the associated equivariant estimate of shift. Asymptotic theory is developed to provide large sample approximations for the null distribution of the test and to provide a framework for a discussion of efficiency. Tests are then developed for the one-way and two-way lay-outs.
On the Convergence of Monte Carlo Maximum Likelihood Calculations
Authors: Geyer Charles J. 
Published: December 2018

SUMMARY
Monte Carlo maximum likelihood for normalized families of distributions can be used for an extremely broad class of models. Given any family {hθ: θ ∈ θ} of non-negative integrable functions, maximum likelihood estimates in the family obtained by normalizing the functions to integrate to 1 can be approximated by Monte Carlo simulation, the only regularity conditions being a compactification of the parameter space such that the evaluation maps θ → hθ(x) remain continuous. Then with probability 1 the Monte Carlo approximant to the log-likelihood hypoconverges to the exact log-likelihood, its maximizer converges to the exact maximum likelihood estimate, approximations to profile likelihoods hypoconverge to the exact profile and level sets of the approximate likelihood (support regions) converge to the exact sets (in Painlevé-Kuratowski set convergence). The same results hold when there are missing data if a Wald-type integrability condition is satisfied. Asymptotic normality of the Monte Carlo error and convergence of the Monte Carlo approximation to the observed Fisher information are also shown.
On the Geometric Convergence of the Gibbs Sampler
Authors: Roberts Gareth O. , Polson Nicholas G. 
Published: December 2018

SUMMARY
The rate of convergence of the Gibbs sampler is discussed. The Gibbs sampler is a Monte Carlo simulation method with extensive application to computational issues in the Bayesian paradigm. Conditions for the geometric rate of convergence of the algorithm for discrete and continuous parameter spaces are derived, and an illustrative exponential family example is given.
Quantifying the Influence of Initial Values on Non-Linear Prediction
Authors: Yao Qiwei , Tong Howell 
Published: December 2018

SUMMARY
Motivated by the m-step-ahead prediction problem in non-linear time series, a brief sketch of stochastic chaotic systems is provided. The accuracy of the prediction depends on the initial value, which is a typical feature of non-linear but not necessarily chaotic models. However, if the model is chaotic, small noise can be amplified very quickly through time evolution at some initial values, thereby decreasing dramatically the reliability of the prediction. Further, if the model is chaotic, small shifts in some initial values can lead to considerable errors in prediction, which can be monitored by the newly defined Lyapunov-like indices. For the nonparametric predictor constructed by the locally linear regression method, the mean-squared error may be decomposed into two parts: the conditional variance and the divergence resulting from a small shift in initial values. The decomposition also holds for more general predictors. A consistent estimator of the Lyapunov-like index is also constructed by the locally linear regression method. Both simulated and real data are used as illustrations.
Finding Words with Unexpected Frequencies in Deoxyribonucleic Acid Sequences
Authors: Prum Bernard , Rodolphe François , De Turckheim Elisabeth 
Published: December 2018

SUMMARY
Considering a Markov chain model for deoxyribonucleic acid sequences, this paper proposes two asymptotically normal statistics to test whether the frequency of a given word is concordant with the first-order Markov chain model or not. The problem is to choose estimates μ^(W) of the expectation of the frequency Mw of a word W in the observed sequence such that the asymptotic variance of MW−μ^(W) is easily computable. The first estimator is derived from the frequency of W [– 1], which is W with its last letter deleted. The second, following an idea of Cowan, is the conditional expectation Mw given the observed frequencies of all two-letter words. Two examples on phage lambda and phage T7 are shown.
Fast Approximate Maximum A Posteriori Restoration of Multicolour Images
Authors: Ferrari Pablo A. , Frigessi Arnoldo , Sá Paula Gonzaga 
Published: December 2018

SUMMARY
We propose a new algorithm for the approximation of the maximum a posteriori (MAP) restoration of noisy images. The image restoration problem is considered in a Bayesian setting. We assume as prior distribution multicolour Markov random fields on a graph whose main restriction is the presence of only pairwise site interactions. The noise is modelled as a Bernoulli field. Computing the mode of the posterior distribution is NP complete, i.e. can (very likely) be done only in a time exponential in the number of sites of the underlying graph. Our algorithm runs in polynomial time and is based on the coding of the colours. It produces an image with the following property: either a pixel is coloured with one of the possible colours or it is left blank. In the first case we prove that this is the colour of the site in the exact MAP restoration. The quality of the approximation is then measured by the number of sites being left blank. We assess the performance of the new algorithm by numerical experiments on the simple three-colour Potts model. More rigorously, we present a probabilistic analysis of the algorithm. The results indicate that the approximation is quite often sufficiently good for the interpretation of the image.
Approximate Random Variate Generation from Infinitely Divisible Distributions with Applications to Bayesian Inference
Authors: Damien Paul , Laud Purushottam W. , Smith Adrian F. M. 
Published: December 2018

SUMMARY
Stochastic processes with independent increments play a central role in Bayesian nonparametric inference. The distributions of the increments of these processes, aside from fixed points of discontinuity, are infinitely divisible and their Laplace and/or Fourier transforms in the Lévy representation are usually known. Conventional Bayesian inference in this context has been limited largely to providing point estimates of the random quantities of interest, although Markov chain Monte Carlo methods have been used to obtain a fuller analysis in the context of Dirichlet process priors. In this paper, we propose and implement a general method for simulating infinitely divisible random variates when their Fourier or Laplace transforms are available in the Lévy representation. Theoretical justification is established by proving a convergence theorem that is a ‘sampling form’ of a classical theorem in probability. The results provide a method for implementing Bayesian nonparametric inference by using a wide range of stochastic processes as priors.
Maximum Likelihood Estimation of the Differencing Parameter for Invertible Short and Long Memory Autoregressive Integrated Moving Average Models
Authors: Beran Jan 
Published: December 2018

SUMMARY
In practical applications of Box-Jenkins autoregressive integrated moving average (ARIMA) models, the number of times that the observed time series must be differenced to achieve approximate stationarity is usually determined by careful, but mostly informal, analysis of the differenced series. For many time series, some differencing seems appropriate, but taking the first or the second difference may be too strong. As an alternative, Hosking, and Granger and Joyeux proposed the use of fractional differences. For -1/2 &lt; d &lt; 1/2, the resulting fractional ARIMA processes are stationary. For 0 &lt; d &lt; 1/2, the correlations are not summable. The parameter d can be estimated, for instance by maximum likelihood. Unfortunately, estimation methods known so far have been restricted to the stationary range -1/2 &lt; d &lt; 1/2. In this paper, we show how any real d &gt; -1/2 can be estimated by an approximate maximum likelihood method. We thus obtain a unified approach to fitting traditional Box-Jenkins ARIMA processes as well as stationary and non-stationary fractional ARIMA processes. A confidence interval for d can be given. Tests, such as for unit roots in the autoregressive parameter or for stationarity, follow immediately. The resulting confidence intervals for the ARMA parameters take into account the additional uncertainty due to estimation of d. A simple algorithm for calculating the estimate of d and the ARMA parameters is given. Simulations and two data examples illustrate the results.
The Usefulness of Optimum Experimental Designs
Authors: Atkinson A. C. 
Published: December 2018

SUMMARY
Optimum experimental designs were originally developed by Kiefer, mainly for response surface models. This survey of recent developments emphasizes potential or actual usefulness. For linear models the construction of exact designs, particularly over irregular design regions, is stressed, as is the blocking of response surface designs. Other important areas include systematic designs that are robust against trend and designs for mixtures with irregular design regions: several industrial examples are mentioned. Both D- and c-optimum designs are found for a non-linear model of the economic response of cereal production to fertilizer level, the c-optimum design being for the conditions of maximum economic return. Locally optimum and Bayesian designs are both described. Similar results for generalized linear models lead to designs for the LD95 in a logistic model in which male and female subjects respond differently. Designs with structure in the variance suggest alternatives to the potentially wasteful product designs of Taguchi. Designs for sequential clinical trials to include random balance are presented. The last section outlines some applications, including life testing and models in which time is a factor.
Reducing the Use of Laboratory Animals in Toxicological Research and Testing by Better Experimental Design
Authors: Festing Michael F. W. , Lovell David P. 
Published: December 2018

SUMMARY
More than 50 million animals are used in biomedical research in the world each year. It is highly desirable that this number is reduced both for ethical and for economic reasons. Better experimental design could lead to the use of fewer animals and improve the repeatability of animal experiments so that alternative methods would be easier to validate. Screening experiments aimed at identifying rodent carcinogens would be more powerful if more than one strain of mice and/or rats were used. Attempts to validate alternative test methods by using chemicals already tested in the Draize test for eye irritation are complicated by limited information on the interexperiment variability of the whole animal test. In academic toxicological research, surveys suggest that many experiments are poorly designed, and some seem to be unnecessarily large.
Sequential Iterated Bootstrap Confidence Intervals
Authors: Lee Stephen M. S. , Young G. Alastair 
Published: December 2018

SUMMARY
A version of the sequential probability ratio test for testing simultaneously a set of nested hypotheses is developed. This procedure is then applied to define a sequential procedure of sampling at the inner level of the two nested levels of resampling required by Monte Carlo construction of an iterated bootstrap percentile method confidence interval. The sequential resampling scheme reduces very significantly the computational demands of construction of the iterated bootstrap confidence interval. The scheme may be applied, simply and without any adaptation, to construct a confidence interval for any parameter of interest. The performance of the sequential iterated bootstrap confidence interval is illustrated on two examples involving the ratio of two population means and a population variance.
Coherent Analysis of Forensic Identification Evidence
Authors: Dawid A. P. , Mortera J. 
Published: December 2018

SUMMARY
A murder has been committed and there is a known population of possible suspects. The identification evidence available, based on information at the scene of the crime, is that the criminal may have a certain characteristic. Information may also be available on a set of suspects about which of them have the characteristic. We discuss the problem of assessing who is the guilty party, taking particular account of the following complicating features: the evidence at the scene of the crime may be unreliable; the suspects examined may have been chosen by means of a search process, which might itself be informative. We also examine the effect of the assumed population dependence structure for the relevant identification characteristics.
Vector Generalized Additive Models
Authors: Yee T. W. , Wild C. J. 
Published: December 2018

SUMMARY
Vector smoothing is used to extend the class of generalized additive models in a very natural way to include a class of multivariate regression models. The resulting models are called ‘vector generalized additive models‘. The class of models for which the methodology gives generalized additive extensions includes the multiple logistic regression model for nominal responses, the continuation ratio model and the proportional and non-proportional odds models for ordinal responses, and the bivariate probit and bivariate logistic models for correlated binary responses. They may also be applied to generalized estimating equations.
Linear Growth Curve Analysis Based on Exponential Dispersion Models
Authors: Jørgensen Bent , Labouriau Rodrigo , Lundbye-Christensen SØren 
Published: December 2018

SUMMARY
The paper introduces a class of linear growth curve models based on combinations of exponential dispersion models. The models have three components, namely a latent Markov growth process with stationary and independent increments, a noise component and a component describing variation between subjects. The models allow a wide range of continuous, discrete and mixed distributions. Estimation for the models is based on an estimating equation derived from an EM-like algorithm combined with Kalman smoothing.
Language, Liberalism and the Critical Religion Challenge
Authors: Bonotti Matteo 
Published: December 2018

AbstractIn this article I consider Cécile Laborde's response to the semantic and Protestant critiques of liberalism's approach to religion. In Section 1 I examine the complex relationship between language and religion that emerges from Laborde's response to the semantic critique. In Section 2 I argue that Laborde's response to the Protestant critique either fails on its own terms or is in tension with her response to the semantic critique. Finally, in Section 3 I critically reflect on the negative conception of freedom that underlies Laborde's response to the Protestant critique, and ask how this can be reconciled with the republican understanding of freedom that characterises Laborde's earlier work.
Food preference and olfactory discrimination tests: A complementary approach to understand the drivers of hedonic responses in dogs
Authors: Basque Caroline , Cambou Stéphanie , Peron Franck , Le Paih Laure , Marzin Cécile , Hanaoka Karine , Callejon Laurence , Prost Carole , Lethuaut Laurent 
Published: December 2018

AbstractUnderstanding the food preference of animals remains a challenge in sensory science. Here, a complementary approach combining food preference and food odor discrimination tests was applied to dogs as consumers. This approach was designed to distinguish a lack of preferential choice from a lack of discrimination in dogs. Consumption of four pet food products by a taster dog panel was evaluated. First choice and intake ratio results were strongly linked, suggesting that odor perception influenced the dogs' food consumption. Within this test, two products were preferred by the dogs and favored equally. To provide more depth to the interpretation, we used an olfactometer to measure the dogs' discrimination of the four product odors. A panel of dogs, designated the expert panel, was specially trained in olfactory recognition of one of the two equally preferred products. The main results demonstrated that dogs can discriminate products by olfaction and express the same food preference for different products, even if they have different odors. Moreover, the olfactory experiments allowed us to conclude that the differences in intensity among odors did not affect their discrimination.Practical applicationsThe combination of a two‐bowl consumption test and an olfactory discrimination task using a dual‐port olfactometer appears to be a complementary approach that could help clarify the drivers of animals' liking. In dogs, for example, olfaction is thought to play a major role in food preference. Therefore, using an automated olfactometer technique provides an opportunity to control and modulate the odors presented to the animal and further explore the role of olfaction in the discrimination of different odorants. Thus, the proposed method could be useful to measure the influence of odor on dogs' food selection and to discern a real absence of preference from a lack of olfactory discrimination capacity, which is currently an important methodological obstacle to analyzing the results of two‐bowl consumption tests. In addition, this complementary approach could be transferred to further works dealing with the understanding of food preference mechanisms in other species.
Suppression of TLR4 by miR-448 is involved in Diabetic development via regulating Macrophage polarization
Authors: Zhao Qing , Wang Xin , Hu Qiaosheng , Zhang Ridong , Yin Yong 
Published: December 2018

Abstract

Objectives
Lipopolysaccharide (LPS) contributed to the development and progression of type 2 diabetes mellitus (T2D), while TLR4 is reported to mediate the LPS-induced inflammation in macrophages. However, the potential molecular mechanisms for TLR4-mediated macrophages activation in T2D have not yet to be fully clarified.

Methods
Type 2 diabetes models in C57BL/6J mice were generated by a combination administration of streptozotocin (STZ) and a high-fat diet (HFD). Cell proportions of M1 and M2 macrophages were analyzed using flow cytometry. Expression profiles of miR-448 and TLR 4 were determined by qRT-PCR and Western blot.

Key findings
LPS/IFN-γ significantly induced M1 polarization in macrophages characterized by the increased levels of TNF-α, IL-6, IL-12, iNOS and decreased levels of TNF-β, CCL-22, IL-10 and Arg-1, with a higher expression of toll-like receptor 4 (TLR4) in vitro. Consistently, T2D mice-derived macrophages had a significantly elevated expression of TLR4 mRNA and decreased expression of miR-448. We further confirmed that miR-448 could inhibit TLR4 expression by targeting the 3′-UTR of TLR4, rescuing the LPS/IFN-γ-induced M1 macrophage polarization.

Conclusions
Taken together, our results indicated that decreased miR-448 in diabetic macrophages may contribute to LPS-induced M1 polarization by targeting TLR4, thereby modulating T2D development.