LEADER 00917nam0-22003131i-450- 001 990000838210403321 005 20001010 010 $a0-8176-3311-1 035 $a000083821 035 $aFED01000083821 035 $a(Aleph)000083821FED01 035 $a000083821 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $a<>Non-Euclidean Revolution with an Introduction by H.S.M. Coxeter$fRichard J. Tru- deau 210 $aBoston$cBirkhauser$d1987 215 $aXII, 269 p. 257 ill.$d25 cm 676 $a516.9 700 1$aTrudeau,$bRichard J.$041089 702 1$aCoxeter,$bH. S. M. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000838210403321 952 $a02 13 A 18$b7392$fFINBN 959 $aFINBN 996 $aNon-Euclidean Revolution with an Introduction by H.S.M. Coxeter$9347930 997 $aUNINA DB $aING01 LEADER 03171nam 2200721Ia 450 001 9910961130403321 005 20200520144314.0 010 $a1-04-020464-3 010 $a0-429-13390-1 010 $a1-283-31148-8 010 $a9786613311481 010 $a1-4200-9966-3 024 7 $a10.1201/b10956 035 $a(CKB)2670000000122506 035 $a(EBL)1633203 035 $a(SSID)ssj0000546079 035 $a(PQKBManifestationID)11391364 035 $a(PQKBTitleCode)TC0000546079 035 $a(PQKBWorkID)10509898 035 $a(PQKB)11275703 035 $a(Au-PeEL)EBL1633203 035 $a(CaPaEBR)ebr10502499 035 $a(CaONFJC)MIL331148 035 $a(OCoLC)740912851 035 $a(OCoLC)759865776 035 $a(OCoLC)1289859176 035 $a(FINmELB)ELB140752 035 $a(MiAaPQ)EBC1633203 035 $a(EXLCZ)992670000000122506 100 $a20110630d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical inference $ethe minimum distance approach /$fAyanendranath Basu, Hiroyuki Shioya, Chanseok Park 205 $a1st ed. 210 $aBoca Raton, FL $cCRC Press$dc2011 215 $a1 online resource (424 p.) 225 1 $aMonographs on statistics and applied probability ;$v120 300 $aA Chapman & Hall book. 311 08$a1-4200-9965-5 320 $aIncludes bibliographical references. 327 $aFront Cover; Dedication; Contents; Preface; Acknowledgments; 1. Introduction; 2. Statistical Distances; 3. Continuous Models; 4. Measures of Robustness and Computational Issues; 5. The Hypothesis Testing Problem; 6. Techniques for Inlier Modification; 7. Weighted Likelihood Estimation; 8. Multinomial Goodness-of-Fit Testing; 9. The Density Power Divergence; 10. Other Applications; 11. Distance Measures in Information and Engineering; 12. Applications to Other Models; Bibliography 330 $aIn many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by ""Minimum Distance Estimation"" is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews developments in density-based minimum distance inference for independently and identically distributed data. Extensions to other more complex models are also discussed. Compr 410 0$aMonographs on statistics and applied probability ;$v120. 606 $aEstimation theory 606 $aDistances 615 0$aEstimation theory. 615 0$aDistances. 676 $a519.5/44 686 $aCOM000000$aMAT029000$2bisacsh 686 $aMAT 625f$2stub 700 $aBasu$b Ayanendranath$0517806 701 $aShioya$b Hiroyuki$0517807 701 $aPark$b Chanseok$0517808 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910961130403321 996 $aStatistical inference$9848753 997 $aUNINA