Newtonian perturbations on models with matter creation

Creation of cold dark matter (CCDM) can macroscopically be described by a negative pressure, and, therefore, the mechanism is capable to accelerate the Universe, without the need of an additional dark energy component. In this framework, we discuss the evolution of perturbations by considering a Neo-Newtonian approach where, unlike in the standard Newtonian cosmology, the fluid pressure is taken into account even in the homogeneous and isotropic background equations (Lima, Zanchin, and Brandenberger, MNRAS 291, L1, 1997). The evolution of the density contrast is calculated in the linear approximation and compared to the one predicted by the Lambda CDM model. The difference between the CCDM and Lambda CDM predictions at the perturbative level is quantified by using three different statistical methods, namely: a simple chi(2)-analysis in the relevant space parameter, a Bayesian statistical inference, and, finally, a Kolmogorov-Smirnov test. We find that under certain circumstances, the CCDM scenario analyzed here predicts an overall dynamics (including Hubble flow and matter fluctuation field) which fully recovers that of the traditional cosmic concordance model. Our basic conclusion is that such a reduction of the dark sector provides a viable alternative description to the accelerating Lambda CDM cosmology.

Estimating wave spectra from the motions of moored vessels: Experimental validation

The practicability of estimating directional wave spectra based on a vessel`s 1st order response has been recently addressed by several researchers. Different alternatives regarding statistical inference methods and possible drawbacks that could arise from their application have been extensively discussed, with an apparent preference for estimations based on Bayesian inference algorithms. Most of the results on this matter, however, rely exclusively on numerical simulations or at best on few and sparse full-scale measurements, comprising a questionable basis for validation purposes. This paper discusses several issues that have recently been debated regarding the advantages of Bayesian inference and different alternatives for its implementation. Among those are the definition of the best set of input motions, the number of parameters required for guaranteeing smoothness of the spectrum in frequency and direction and how to determine their optimum values. These subjects are addressed in the light of an extensive experimental campaign performed with a small-scale model of an FPSO platform (VLCC hull), which was conducted in an ocean basin in Brazil. Tests involved long and short crested seas with variable levels of directional spreading and also bimodal conditions. The calibration spectra measured in the tank by means of an array of wave probes configured the paradigm for estimations. Results showed that a wide range of sea conditions could be estimated with good precision, even those with somewhat low peak periods. Some possible drawbacks that have been pointed out in previous works concerning the viability of employing large vessels for such a task are then refuted. Also, it is shown that a second parameter for smoothing the spectrum in frequency may indeed increase the accuracy in some situations, although the criterion usually proposed for estimating the optimum values (ABIC) demands large computational effort and does not seem adequate for practical on-board systems, which require expeditious estimations. (C) 2009 Elsevier Ltd. All rights reserved.

Directional Wave Spectrum Estimation Based on Vessel`s 1st-order Motions: Field Results

In this paper, 2 different approaches for estimating the directional wave spectrum based on a vessel`s 1st-order motions are discussed, and their predictions are compared to those provided by a wave buoy. The real-scale data were obtained in an extensive monitoring campaign based on an FPSO unit operating at Campos Basin, Brazil. Data included vessel motions, heading and tank loadings. Wave field information was obtained by means of a heave-pitch-roll buoy installed in the vicinity of the unit. `two of the methods most widely used for this kind of analysis are considered, one based on Bayesian statistical inference, the other consisting of a parametrical representation of the wave spectrum. The performance of both methods is compared, and their sensitivity to input parameters is discussed. This analysis complements a set of previous validations based on numerical and towing-tank results and allows for a preliminary evaluation of reliability when applying the methodology at full scale.

Nasal allergies in the Latin American population: Results from the Allergies in Latin America survey

Allergies in Latin America is the first cross-national survey that describes the symptoms, impact, and treatment of nasal allergies (NAs) in individuals >= 4 years old in Latin America (LA). In total, 22,012 households across the Latin American countries of Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Venezuela were screened for children, adolescents, and adults with a diagnosis of NA and either symptoms or treatment in the past 12 months. A total of 1088 adults and 457 children and adolescents were included and the sample was probability based to ensure valid statistical inference to the population. Approximately 7% of the LA population was diagnosed with NAs with two of three respondents stating that their allergies were seasonal or intermittent in nature. A general practice physician or otolaryngologist diagnosed the majority of individuals surveyed. Nasal congestion was the most common and bothersome symptom of NAs. Sufferers indicated that their symptoms affected productivity and sleep and had a negative impact on quality of life. Two-thirds of patients reported taking some type of medication for their NAs, with a roughly equal percentage of patients reporting taking over-the-counter versus prescription medications. Changing medications was most commonly done in those reporting inadequate efficacy. The most common reasons cited for dissatisfaction with current medications were related to inadequate effectiveness, effectiveness wearing off with chronic use, failure to provide 24-hour relief, and bothersome side effects (e.g., unpleasant taste and retrograde drainage into the esophagus). Findings from this cross-national survey on NAs have confirmed a high prevalence of physician-diagnosed NAs and a considerable negative impact on daily quality of life and work productivity as well as substantial disease management challenges in LA. Through identification of disease impact on the LA population and further defining treatment gaps, clinicians in LA may better understand and treat NAs, thus leading to improvements in overall patient satisfaction and quality of life. (Allergy Asthma Proc 31:S9-S27, 2010; doi: 10.2500/aap.2010.31.3347)

On the mass distribution of neutron stars

The distribution of masses for neutron stars is analysed using the Bayesian statistical inference, evaluating the likelihood of the proposed Gaussian peaks by using 54 measured points obtained in a variety of systems. The results strongly suggest the existence of a bimodal distribution of the masses, with the first peak around 1.37 M(circle dot) and a much wider second peak at 1.73 M(circle dot). The results support earlier views related to the different evolutionary histories of the members for the first two peaks, which produces a natural separation (even if no attempt to `label` the systems has been made here). They also accommodate the recent findings of similar to M(circle dot) masses quite naturally. Finally, we explore the existence of a subgroup around 1.25 M(circle dot), finding weak, if any, evidence for it. This recently claimed low-mass subgroup, possibly related to the O-Mg-Ne core collapse events, has a monotonically decreasing likelihood and does not stand out clearly from the rest of the sample.

Random number generators for the generalized Birnbaum-Saunders distribution

The generalized Birnbaum-Saunders distribution pertains to a class of lifetime models including both lighter and heavier tailed distributions. This model adapts well to lifetime data, even when outliers exist, and has other good theoretical properties and application perspectives. However, statistical inference tools may not exist in closed form for this model. Hence, simulation and numerical studies are needed, which require a random number generator. Three different ways to generate observations from this model are considered here. These generators are compared by utilizing a goodness-of-fit procedure as well as their effectiveness in predicting the true parameter values by using Monte Carlo simulations. This goodness-of-fit procedure may also be used as an estimation method. The quality of this estimation method is studied here. Finally, through a real data set, the generalized and classical Birnbaum-Saunders models are compared by using this estimation method.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented. (C) 2011 Elsevier B.V. All rights reserved.

Reduction of Models in the Presence of Nuisance Parameters

In many statistical inference problems, there is interest in estimation of only some elements of the parameter vector that defines the adopted model. In general, such elements are associated to measures of location and the additional terms, known as nuisance parameters, to control the dispersion and asymmetry of the underlying distributions. To estimate all the parameters of the model and to draw inferences only on the parameters of interest. Depending on the adopted model, this procedure can be both algebraically is common and computationally very costly and thus it is convenient to reduce it, so that it depends only on the parameters of interest. This article reviews estimation methods in the presence of nuisance parameters and consider some applications in models recently discussed in the literature.

INFERENCE FOR EIGENVALUES AND EIGENVECTORS OF GAUSSIAN SYMMETRIC MATRICES

This article presents maximum likelihood estimators (MLEs) and log-likelihood ratio (LLR) tests for the eigenvalues and eigenvectors of Gaussian random symmetric matrices of arbitrary dimension, where the observations are independent repeated samples from one or two populations. These inference problems are relevant in the analysis of diffusion tensor imaging data and polarized cosmic background radiation data, where the observations are, respectively, 3 x 3 and 2 x 2 symmetric positive definite matrices. The parameter sets involved in the inference problems for eigenvalues and eigenvectors are subsets of Euclidean space that are either affine subspaces, embedded submanifolds that are invariant under orthogonal transformations or polyhedral convex cones. We show that for a class of sets that includes the ones considered in this paper, the MLEs of the mean parameter do not depend on the covariance parameters if and only if the covariance structure is orthogonally invariant. Closed-form expressions for the MLEs and the associated LLRs are derived for this covariance structure.

Robust inference in an heteroscedastic measurement error model

In this paper we deal with robust inference in heteroscedastic measurement error models Rather than the normal distribution we postulate a Student t distribution for the observed variables Maximum likelihood estimates are computed numerically Consistent estimation of the asymptotic covariance matrices of the maximum likelihood and generalized least squares estimators is also discussed Three test statistics are proposed for testing hypotheses of interest with the asymptotic chi-square distribution which guarantees correct asymptotic significance levels Results of simulations and an application to a real data set are also reported (C) 2009 The Korean Statistical Society Published by Elsevier B V All rights reserved

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Inference and local influence assessment in skew-normal null intercept measurement error model

In this article, we discuss inferential aspects of the measurement error regression models with null intercepts when the unknown quantity x (latent variable) follows a skew normal distribution. We examine first the maximum-likelihood approach to estimation via the EM algorithm by exploring statistical properties of the model considered. Then, the marginal likelihood, the score function and the observed information matrix of the observed quantities are presented allowing direct inference implementation. In order to discuss some diagnostics techniques in this type of models, we derive the appropriate matrices to assessing the local influence on the parameter estimates under different perturbation schemes. The results and methods developed in this paper are illustrated considering part of a real data set used by Hadgu and Koch [1999, Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161-178].

Epsilon Birnbaum-Saunders distribution family: properties and inference

In this paper we introduce a new extension for the Birnbaum-Saunder distribution based on the family of the epsilon-skew-symmetric distributions studied in Arellano-Valle et al. (J Stat Plan Inference 128(2):427-443, 2005). The extension allows generating Birnbaun-Saunders type distributions able to deal with extreme or outlying observations (Dupuis and Mills, IEEE Trans Reliab 47:88-95, 1998). Basic properties such as moments and Fisher information matrix are also studied. Results of a real data application are reported illustrating good fitting properties of the proposed model.

Improved likelihood inference in beta regression

We consider the issue of performing accurate small-sample likelihood-based inference in beta regression models, which are useful for modelling continuous proportions that are affected by independent variables. We derive small-sample adjustments to the likelihood ratio statistic in this class of models. The adjusted statistics can be easily implemented from standard statistical software. We present Monte Carlo simulations showing that inference based on the adjusted statistics we propose is much more reliable than that based on the usual likelihood ratio statistic. A real data example is presented.