this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

We develop a novel and general approach to estimating the accuracy of protein multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new problem that we call parameter advising. For protein alignments, we consider twelve independent features that contribute to a quality alignment. An accuracy estimator is learned that is a polynomial function ...

Abstract We develop a novel and general approach to estimating the accuracy of multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new task that we call parameter advising: the problem of choosing values for alignment scoring function parameters from a given set of choices to maximize the accuracy of a computed alignment. For protein alignm...

In this research paper, we have discussed the estimation of location and scale parameters of Weibull distribution using concept of generalized order statistics by the best linear unbiased estimator (BLUE) and an alternative linear estimates (Gupta) from Type II singly and doubly censored samples. Tables of coefficients for best linear estimator and alternative estimator of μ and λ have been obt...

This paper is concerned with estimation of a predictive density with parametric constraints under Kullback-Leibler loss. When an invariance structure is embedded in the problem, general and unified conditions for the minimaxity of the best equivariant predictive density estimator are derived. These conditions are applied to check minimaxity in various restricted parameter spaces in location and...

This paper investigates four receiver architectures and their ability to track the carrier phase signal under attenuated environments. The receiver architectures considered include a standard receiver, an estimator-based receiver, a vector-based receiver and an ultra-tight GPS/IMU receiver. The receiver architectures were implemented in software and tested using pedestrianbased data collected i...

Abstract A new method for combining several initial estimators of the regression function is introduced. Instead of building a linear or convex optimized combination over a collection of basic estimators r1, . . . , rM , we use them as a collective indicator of the distance between the training data and a test observation. This local distance approach is model-free and extremely fast. Most impo...

We propose a model selection approach for covariance estimation of a multidimensional stochastic process. Under very general assumptions, observing i.i.d replications of the process at fixed observation points, we construct an estimator of the covariance function by expanding the process onto a collection of basis functions. We study the non asymptotic property of this estimate and give a tract...

In this paper, we propose the weighted moments estimators (WMEs) of the scale parameter of a Pareto distribution with known shape parameter under the type II multiply censored sample. Moreover, we give a computational comparison for these proposed WMEs and best linear unbiased estimator (BLUE) of the scale parameter on the basis of the exact mean squared error (MSE) for given sample sizes and d...

The Liu estimator has consistently been demonstrated to be an attractive shrinkage method for reducing the effects of multicollinearity. The Poisson regression model is a well-known model in applications when the response variable consists of count data. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator (MLE) of the Poisson regressio...