53 research outputs found

    Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function

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    AbstractIn this article, a family of feasible generalized double k-class estimator in a linear regression model with non-spherical disturbances is considered. The performance of this estimator is judged with feasible generalized least-squares and feasible generalized Stein-rule estimators under balanced loss function using the criteria of quadratic risk and general Pitman closeness. A Monte-Carlo study investigates the finite sample properties of several estimators arising from the family of feasible double k-class estimators

    BAYESIAN INFERENCE FOR STATE SPACE MODEL WITH PANEL DATA

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    The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making

    A Hybrid Approach for Network Selection and Fast Delivery Handover Route

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    The enthusiasm for run resources has extended essentially with the presence of present day remote applications. Range sharing, considered as an essential instrument for 5G frameworks, is envisioned to address extend deficiency issue, achieve high data rate get to, and guaranteed Quality of Service (QoS). From the authorized system's point of view, the obstruction caused by all secondary users (SUs) ought to be limited. From optional systems perspective, there is a need to role out systems to SUs such that general impedance is diminished, empowering the settlement of a developing number of SUs. This paper presents a network and Fast Delivery Handover Route in order to less no of iteration to optimize fitness value and decision weight, in terms of RSSI, Loss, bandwidth, Speed, crossover rate and cost. To solve the optimization problem, Particle swarm optimization and Neural Network are used. At last, the paper is upheld by broad recreation comes about which show the adequacy of the proposed techniques in finding a close ideal arrangement

    Confidence ellipsoids based on a general family of shrinkage estimators for a linear model with non-spherical disturbances

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    AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of a linear regression model with nonspherical disturbances, and derives estimators for the Mean Squared Error (MSE) matrix, and risk under quadratic loss for this family of estimators. The confidence ellipsoids for the coefficient vector based on this family of estimators are proposed, and the performance of the confidence ellipsoids under the criterion of coverage probability and expected volumes is investigated. The results of a numerical simulation are presented to illustrate the theoretical findings, which could be applicable in the area of economic growth modeling

    Unit Root Test for Panel Data AR(1) Time Series Model With Linear Time Trend and Augmentation Term: A Bayesian Approach

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    The univariate time series models, in the case of unit root hypothesis, are more biased towards the acceptance of the Unit Root Hypothesis especially in a short time span. However, the panel data time series model is more appropriate in such situation. The Bayesian analysis of unit root testing for a panel data time series model is considered. An autoregressive panel data AR(1) model with linear time trend and augmentation term has been considered and derived the posterior odds ratio for testing the presence of unit root hypothesis under appropriate prior assumptions. A simulation study and real data analysis are carried out for the derived theorem

    Robust linear static panel data models using epsilon-contamination

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    The paper develops a general Bayesian framework for robust linear static panel data models using epsilon-contamination. A two-step approach is employed to derive the conditional type II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior densities are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good performance of the three-stage hierarchy estimator. Within a single theoretical framework, our Bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case. We illustrate the performance of our estimator relative to classic panel estimators using data on earnings and crime
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