LEADER 00958nam0 22002171i 450 001 UON00180566 005 20231205103130.158 100 $a20030730d1982 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| 1|||| 200 1 $aˆIl ‰"caso" Salvatore Di Bartolo teologo palermitano$fFrancesco Conigliaro 210 $aPa lermo$cMazzone$d1982 - 132 p. ; 22 cm. 606 $aDI BARTOLO SALVATORE$3UONC037059$2FI 700 1$aConigliaro$bFrancesco$3UONV101740 801 $aIT$bSOL$c20250314$gRICA 912 $aUON00180566 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI EUR D B 0313 $eSI SC 13616 5 0313 $sBuono 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI EUR D B 0313 $eSI SC 13759 5 0313 $sBuono 996 $a"caso" Salvatore di Bartolo teologo palermitano$9639968 997 $aUNIOR LEADER 03435nam 22005895 450 001 9910254274903321 005 20250402095936.0 010 $a981-10-5296-4 024 7 $a10.1007/978-981-10-5296-5 035 $a(CKB)4340000000062071 035 $a(DE-He213)978-981-10-5296-5 035 $a(MiAaPQ)EBC4926946 035 $a(PPN)203667239 035 $a(EXLCZ)994340000000062071 100 $a20170727d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Estimation for Truncated Exponential Families /$fby Masafumi Akahira 205 $a1st ed. 2017. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2017. 215 $a1 online resource (XI, 122 p. 10 illus.) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$a981-10-5295-6 320 $aIncludes bibliographical references at the end of each chapters and index. 330 $aThis book presents new findings on nonregular statistical estimation. Unlike other books on this topic, its major emphasis is on helping readers understand the meaning and implications of both regularity and irregularity through a certain family of distributions. In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. This focus includes the (truncated) Pareto distribution, which is widely used in various fields such as finance, physics, hydrology, geology, astronomy, and other disciplines. The family is essential in that it links both regular and nonregular distributions, as it becomes a regular exponential family if the truncation parameter is known. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter. In order to obtain more information on the truncation, the Bayesian approach is also considered. Further, the application to some useful truncated distributions is discussed. The illustrated clarification of the nonregular structure provides researchers and practitioners with a solid basis for further research and applications. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.544 700 $aAkahira$b Masafumi$4aut$4http://id.loc.gov/vocabulary/relators/aut$0442015 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254274903321 996 $aStatistical Estimation for Truncated Exponential Families$91562246 997 $aUNINA