In this paper, given a progressively type II censored sample from a generalized half logistic distribution, the Bayesian estimators are obtained under asymmetric loss functions such as LINEX loss function, Precautionary loss function, Entropy loss function for the parameter, Reliability and cumulative hazard functions. Comparisons of these estimators are made through simulation studies. Mathema...

Methacholine provocation tests are aimed at determining bronchial responsiveness. Recent investigations stress the importance of considering the entire log-dose response curve, yielding not only the provocative dose producing a 20% change in forced expiratory volume in one second (FEV1) (PC20), but also the plateau value and the steepest slope of the curve (reactivity). In three control subject...

We developed a novel movement-imagery-based braincomputer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, timefrequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were ...

Nonparametric Random Variate Generation Using a Piecewise-Linear Cumulative Distribution Function W. Kaczynski a , L. Leemis b , N. Loehr c & J. McQueston b a Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA b Department of Mathematics, The College of William & Mary, Williamsburg, Virginia, USA c Department of Mathematics, Virginia Polytechnic Insti...

Consider a model parameterized by a scalar parameter of interest and a nuisance parameter vector. Inference about the parameter of interest may be based on the signed root of the likelihood ratio statistic R. The standard normal approximation to the conditional distribution of R typically has error of order O(n−1/2), where n is the sample size. There are several modifications for R, which reduc...

Bivariate Nonparametric Random Variate Generation Using a Piecewise-Linear Cumulative Distribution Function W. Kaczynski a , L. Leemis b , N. Loehr c & J. McQueston b a Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA b Department of Mathematics, The College of William & Mary, Williamsburg, Virginia, USA c Department of Mathematics, Virginia Polytec...

Abstract: The estimation of an unknown cumulative distribution function in the interval censoring “case 1” model from dependent sequences is considered. We construct a new adaptive estimator based on a warped wavelet basis and a hard thresholding rule. Under mild assumptions on the parameters of the model, considering the L2 risk and the weighted Besov balls, we prove that the estimator attains...

The Wrapped package computes the probability density function, cumulative distribution function, quantile function and also generates random samples for many univariate wrapped distributions. It also computes maximum likelihood estimates, standard errors, confidence intervals and measures of goodness of fit for nearly fifty univariate wrapped distributions. Numerical illustrations of the packag...

This paper deals with the mathematical method for extracting Exponential Rayleighh distribution based on mixed between cumulative function of and Rayleigh using an application (maximum), as well derived different statistical properties distribution, present a structure new modified weighted version Azzalini’s (1985) named Modified Weighted such that this is generalization provide some special m...