submitted by legeomorqais 2 years and 3 months ago - Topic: Statistics
In this work the ranked set sampling technique has been applied to estimate the scale parameter
a
of a log-logistic distribution under a situation where the units in a sample can be ordered by judgement method without any error. We have evaluated the Fisher information contained in the order statistics arising from this distribution and observed that median of a random sample contains the maximum information about the parameter
a
. Accordingly we have used median ranked set sampling to estimate
a
. We have further carried out the multistage median ranked set sampling to estimate
a
with improved precision. Suppose it is not possible to rank the units in a sample according to judgement method without error but the units can be ordered based on an auxiliary variable
Z
such that
(X, Z)
has a Morgenstern type bivariate log-logistic distribution (MTBLLD). In such a situation we have derived the Fisher information contained in the concomitant of rth order statistic of a random sample of size
n
from MTBLLD and identified those concomitants among others which possess largest amount of Fisher information and defined an unbalanced ranked set sampling utilizing those units in the sample and thereby proposed an estimator of
a
using the measurements made on those units in this ranked set sample.
Ragab (Microelectronics Reliability, 91–95, 31, 1991) described the Bayesian and empirical Bayesian methods for estimation of the stress–strength parameter R = P ( Y < X ) , when X and Y are independent random variables from two generalized logistic (GL) distributions having the...
A new estimation method for the two-component mixture model introduced inVandekerkhove (2012) is proposed. This model, which consists of a two-componentmixture of linear regressions in which one component is entirely known whilethe proportion, the slope, the intercept and the error distribution o...
A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum like...
In this paper, by considering a progressively Type-II censored sample from the two-parameter Gompertz distribution, a necessary and sufficient condition is established for the existence and uniqueness of the maximum-likelihood estimates of the shape and scale parameters. The results for the speci...
For the first time, a four-parameter beta generalized logistic distribution is obtained by compounding the beta and generalized logistic distributions. The new model extends some well-known distributions and its shape is quite flexible, specially the skewness and the tail weights, due to the extr...
We propose a unified penalized likelihood method which can simultaneously select significant variables in the location and scale models. Furthermore, the proposed variable selection method can simultaneously perform parameter estimation and variable selection in the location and scale models. Wit...
We consider estimation of the mean vector, $theta $, of a spherically symmetric distribution with known scale parameter under quadratic loss and when a residual vector is available. We show minimaxity of generalized Bayes estimators corresponding to superharmonic priors with a non decreasing Lapl...
This paper aims at providing the prior and posterior interpretations for the parameters in the logistic regression model with random or cluster-level intercept when univariate and multivariate classes of skew normal distributions are assumed to model the random effects behavior. We obtain the pri...
Han and Liu (2012) proposed a method named transelliptical component analysis(TCA) for conducting scale-invariant principal component analysis on highdimensional data with transelliptical distributions. The transelliptical familyassumes that the data follow an elliptical distribution after unspec...