LEADER 03066oam 2200673I 450 001 9910789718403321 005 20230725032318.0 010 $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(MiAaPQ)EBC1633203 035 $a(Au-PeEL)EBL1633203 035 $a(CaPaEBR)ebr10502499 035 $a(CaONFJC)MIL331148 035 $a(OCoLC)740912851 035 $a(OCoLC)759865776 035 $a(EXLCZ)992670000000122506 100 $a20180331d2011 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 210 1$aBoca Raton, Fla. :$cChapman & Hall/CRC,$d2011. 215 $a1 online resource (424 p.) 225 1 $aMonographs on statistics and applied probability ;$v120 300 $aA Chapman & Hall book. 311 $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 $a9910789718403321 996 $aStatistical inference$9848753 997 $aUNINA LEADER 00849cam a2200229 i 4500 001 991004319234807536 005 20240423104012.0 008 240423s1927----it ier 000 0 ita d 040 $aBibl. Interfacoltà T. Pellegrino$bita$cSocioculturale Scs 041 0 $aita 082 04$a332.6$223. 100 1 $aChessa, Federico$0103068 245 13$aLa classificazione dei rischi e il rischio dell'impresa /$cFederico Chessa 260 $aRoma :$bTipografia delle terme,$c1927 300 $a127 p. ;$c20 cm 490 1 $aStudi di politica, finanza ed economia 500 $aEstratto da: Rivista di politica economica, 1927, fasc. 2 650 4$aCapitale$xInvestimenti$xRischi 830 0$aStudi di politica, finanza ed economia 912 $a991004319234807536 996 $aClassificazione dei rischi e il rischio dell'impresa$9638373 997 $aUNISALENTO