LEADER 03067nam 2200625 a 450 001 9910812868103321 005 20200520144314.0 010 $a1-281-89246-7 010 $a9786611892463 010 $a81-224-2537-2 035 $a(CKB)1000000000691726 035 $a(EBL)366583 035 $a(OCoLC)476201510 035 $a(SSID)ssj0000673886 035 $a(PQKBManifestationID)11384491 035 $a(PQKBTitleCode)TC0000673886 035 $a(PQKBWorkID)10646684 035 $a(PQKB)10890925 035 $a(MiAaPQ)EBC366583 035 $a(Au-PeEL)EBL366583 035 $a(CaPaEBR)ebr10323371 035 $a(CaONFJC)MIL189246 035 $a(EXLCZ)991000000000691726 100 $a20091007d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust estimation and hypothesis testing /$fMoti L. Tiku, Aysen D. Akkaya 205 $a1st ed. 210 $aNew Delhi $cNew Age International (P) Ltd., Publishers$d2004 215 $a1 online resource (354 p.) 300 $aDescription based upon print version of record. 311 $a81-224-1556-3 320 $aIncludes bibliographical references (p. 308-330) and index. 327 $aCover; Preface; Contents; Chapter 1 Robustness of Some Classical Estimators and Tests; Chapter 2 Estimation of Location and Scale Parameters; Chapter 3 Linear Regression with Normal and Non-normal Error Distributions; Chapter 4 Binary Regression with Logistic and Nonlogistic Density Functions; Chapter 5 Autoregressive Models in Normal and Non-Normal Situations; Chapter 6 Analysis of Variance in Experimental Design; Chapter 7 Censored Samples from Normal and Non-Normal Distributions; Chapter 8 Robustness of Estimators and Tests; Chapter 9 Goodness-of-fit and Detection of Outliers 327 $aChapter 10 Estimation in Sample SurveyChapter 11 Applications; Bibliography; Index 330 $aIn statistical theory and practice, a certain distribution is usually assumed and then optimal solutions sought. Since deviations from an assumed distribution are very common, one cannot feel comfortable with assuming a particular distribution and believing it to be exactly correct. That brings the robustness issue in focus. In this book, we have given statistical procedures which are robust to plausible deviations from an assumed mode. The method of modified maximum likelihood estimation is used in formulating these procedures. The modified maximum likelihood estimators are explicit functions 606 $aRobust statistics 606 $aNonparametric statistics 606 $aEstimation theory 615 0$aRobust statistics. 615 0$aNonparametric statistics. 615 0$aEstimation theory. 676 $a519.5/44 700 $aTiku$b Moti Lal$0102779 701 $aAkkaya$b Aysen D$01702250 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812868103321 996 $aRobust estimation and hypothesis testing$94086658 997 $aUNINA