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RANDOMNESS&OPTIMAL ESTIMATION, by M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache

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M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache RANDOMNESS AND OPTIMAL ESTIMATION IN DATA SAMPLING (second edition) American Research Press Rehoboth 2002 0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 0.05 1 2 3 4 5 6 7 8 ∆ P R E / A R B * 1 0 0 0 PRE ARB*1000 ARB(MMSE Esti.) PRE Cut-off Point 2 M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache RANDOMNESS AND OPTIMAL ESTIMATION IN DATA SAMPLING (second
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    M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. SmarandacheRANDOMNESS AND OPTIMAL ESTIMATIONIN DATA SAMPLING (second edition) American Research Press Rehoboth2002 0.00500.001000.001500.002000.002500.003000.000.0512345678 ∆    P   R   E   /   A   R   B   *   1   0   0   0 PREARB*1000ARB(MMSE Esti.)PRE Cut-off Point   2 M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache   RANDOMNESS AND OPTIMAL ESTIMATIONIN DATA SAMPLING (second edition) Dr. Mohammad Khoshnevisan, Griffith University, School of Accounting andFinance, Qld., Australia.Dr. Housila P. Singh and S. Saxena, School of Statistics, Vikram University,UJJAIN, 456010, India.Dr. Sarjinder Singh Department of Mathematics and statistics.University of Saskatchewan, Canada.Dr. Florentin. Smarandache, Department of Mathematics, UNM, USA.American Research PressRehoboth2002   3 This book can be ordered in microfilm format from:ProQuest Information & Learning(University of Microfilm International)300 N. Zeeb RoadP.O. Box 1346, Ann Arbor MI 48106-1346, USATel.: 1-800-521-0600 (Customer Service)http://wwwlib.umi.com/bod/ (Books on Demand)Copyright 2002 by American Research Press & AuthorsRehoboth, Box 141 NM 87322, USAMany books can be downloaded from our   E-Library of Science :http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htmThis book has been peer reviewed and recommended for publication by:Dr. V. Seleacu, Department of Mathematics / Probability and Statistics, University of Craiova, Romania;Dr. Sabin Tabirca, University College Cork, Department of Computer Science andMathematics, Ireland;Dr. Vasantha Kandasamy, Department of Mathematics, Indian Institute of Technology,Madras, Chennai – 600 036, India. ISBN : 1-931233-68-3 Standard Address Number 297-5092 Printed in the United States of America   4 Forward The purpose of this book is to postulate some theories and test them numerically.Estimation is often a difficult task and it has wide application in social sciences andfinancial market. In order to obtain the optimum efficiency for some classes of estimators, we have devoted this book into three specialized sections:Part 1. In this section we have studied a class of shrinkage estimators for shape parameter beta in failure censored samples from two-parameter Weibull distributionwhen some 'apriori' or guessed interval containing the parameter beta is available inaddition to sample information and analyses their properties. Some estimators aregenerated from the proposed class and compared with the minimum mean squared error (MMSE) estimator. Numerical computations in terms of percent relative efficiency andabsolute relative bias indicate that certain of these estimators substantially improve theMMSE estimator in some guessed interval of the parameter space of beta, especially for censored samples with small sizes. Subsequently, a modified class of shrinkageestimators is proposed with its properties.Part2. In this section we have analyzed the two classes of estimators for populationmedian M Y of the study character Y using information on two auxiliary characters X andZ in double sampling. In this section we have shown that the suggested classes of estimators are more efficient than the one suggested by Singh et al  (2001). Estimators based on estimated optimum values have been also considered with their properties. Theoptimum values of the first phase and second phase sample sizes are also obtained for thefixed cost of survey.Part3. In this section, we have investigated the impact of measurement errors on a familyof estimators of population mean using multiauxiliary information. This error minimization is vital in financial modeling whereby the objective function lies uponminimizing over-shooting and undershooting.This book has been designed for graduate students and researchers who are active in thearea of estimation and data sampling applied in financial survey modeling and appliedstatistics. In our future research, we will address the computational aspects of thealgorithms developed in this book.The Authors

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Aug 22, 2017
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