Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis

Chirp signals are frequently used in different areas of science and engineering. MCMC-based Bayesian inference is done here for the purpose of one-step and multiple-step prediction in the case of the one-dimensional single chirp signal with iid error structure as well as dependent error structure with exponentially decaying covariances. We use Gibbs sampling technique and random walk MCMC to update the parameters. We perform total five simulation studies for the illustration purpose. We also do some real-data analysis to show how the method is working in practice.     

Estimation under inverse Weibull distribution based on Balakrishnan’s unified hybrid censored scheme

In this article, point and interval estimations of the parameters α and β of the inverse Weibull distribution (IWD) have been studied based on Balakrishnan’s unified hybrid censoring scheme (UHCS), see Balakrishnan et al. In point estimation, the maximum likelihood (ML) and Bayes (B) methods have been used. The Bayes estimates have been computed based on squared error loss (SEL) function and Linex loss function and using Markov Chain Monte Carlo (MCMC) algorithm. In interval estimation, a (1 ? τ) × 100% approximate, bootstrap-p, credible and highest posterior density (HPD) confidence intervals (CI′s) for the parameters α and β have been introduced. Based on Monte Carlo simulation, Bayes estimates have been compared with their corresponding maximum likelihood estimates by computing the mean squared errors (MSE′s) of all estimators. Finally, point and interval estimations of all parameters have been studied based on a real data set as an illustrative example.     

Optimal direction Gibbs sampler for truncated multivariate normal distributions

Generalized Gibbs samplers simulate from any direction, not necessarily limited to the coordinate directions of the parameters of the objective function. We study how to optimally choose such directions in a random scan Gibbs sampler setting. We consider that optimal directions will be those that minimize the Kullback–Leibler divergence of two Markov chain Monte Carlo steps. Two distributions over direction are proposed for the multivariate Normal objective function. The resulting algorithms are used to simulate from a truncated multivariate Normal distribution, and the performance of our algorithms is compared with the performance of two algorithms based on the Gibbs sampler.     

Confidence band for percentile line in multiple linear regression

We deal with the problem of estimating constructing a confidence band for the 100γth percentile line in the multiple linear regression model with independent identically normally distributed errors. A method for computing the exact Scheffé type confidence band over a limited space of the particular covariates region is suggested. A confidence band depends on an estimator of the percentile line. The confidence bands based on the different estimators of the percentile line are compared with respect to the average bandwidth.     

Robust generalized confidence intervals

Most interval estimates are derived from computable conditional distributions conditional on the data. In this article, we call the random variables having such conditional distributions confidence distribution variables and define their finite-sample breakdown values. Based on this, the definition of breakdown value of confidence intervals is introduced, which covers the breakdowns in both the coverage probability and interval length. High-breakdown confidence intervals are constructed by the structural method in location-scale families. Simulation results are presented to compare the traditional confidence intervals and their robust analogues.     

Weighted likelihood based inference for P(X < Y)

This contribution deals with the statistical problem of evaluating the stress–strength reliability parameter R = P(X < Y), when both stress and strength data are prone to contamination. Standard likelihood inference can be badly affected by mild data inadequacies, that often occur in the form of several outliers. Then, robust tools are recommended. Here, inference relies on the weighted likelihood methodology. This approach has the advantage to lead to robust estimators, tests, and confidence intervals that share the main asymptotic properties of their classical counterparts. The accuracy of the proposed methodology is illustrated both by numerical studies and real-data applications.     

Poverty comparisons with common relative poverty lines

Relative poverty lines are increasingly being used in poverty comparison studies. Existing methods assume that the distributions being compared are distinct with independent relative poverty lines. However, this practice may be problematic when comparing two subgroups of a population. We follow up on a recent proposal for the usage of common relative poverty lines in such cases, and develop a test for comparing poverty between subgroups of a single population, using inequality restrictions. Monte Carlo experiments are conducted in order to examine the size and power of our proposed test. We illustrate our procedure using some U.S. household income data.     

Heteroskedasticity–robust tests with minimum size distortion

Asymptotically valid inference in linear regression models is easily achieved under mild conditions using the well-known Eicker–White heteroskedasticity–robust covariance matrix estimator or one of its variant. In finite sample however, such inferences can suffer from substantial size distortion. Indeed, it is well established in the literature that the finite sample accuracy of a test may depend on which variant of the Eicker–White estimator is used, on the underlying data generating process (DGP) and on the desired level of the test.

This paper develops a new variant of the Eicker–White estimator which explicitly aims to minimize the finite sample null error in rejection probability (ERP) of the test. This is made possible by selecting the transformation of the squared residuals which results in the smallest possible ERP through a numerical algorithm based on the wild bootstrap. Monte Carlo evidence indicates that this new procedure achieves a level of robustness to the DGP, sample size and nominal testing level unequaled by any other Eicker–White estimator based asymptotic test.     

A weighted stochastic restricted ridge estimator in partially linear model

In this article, we consider the estimation of a partially linear model when stochastic linear restrictions on the parameter components are assumed to hold. Based on the weighted mixed estimator, profile least-squares method, and ridge method, a weighted stochastic restricted ridge estimator of the parametric component is introduced. The properties of the new estimator are also discussed. Finally, a simulation study is given to show the performance of the new estimator.     

On the Flatland paradox and limiting arguments

We revisit the Flatland paradox proposed by Stone (1976 Stone, M. 1976. Strong inconsistency from uniform priors. Journal of the American Statistical Association 71 (353):114–25.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), which is an example of non conglomerability. The main novelty in the analysis of the paradox is to consider marginal versus conditional models rather than proper versus improper priors. We show that in the first model a prior distribution should be considered as a probability measure, whereas, in the second one, a prior distribution should be considered in the projective space of measures. This induces two different kinds of limiting arguments which are useful to understand the paradox. We also show that the choice of a flat prior is not adapted to the structure of the parameter space and we consider an improper prior based on reference priors with nuisance parameters for which the Bayesian analysis matches the intuitive reasoning.