LEADER 03707nam 22007095 450 001 9910728952603321 005 20240320121622.0 010 $a981-9918-62-6 024 7 $a10.1007/978-981-99-1862-1 035 $a(MiAaPQ)EBC30558391 035 $a(Au-PeEL)EBL30558391 035 $a(OCoLC)1381093904 035 $a(DE-He213)978-981-99-1862-1 035 $a(BIP)089626130 035 $a(PPN)270614451 035 $a(CKB)26816401100041 035 $a(EXLCZ)9926816401100041 100 $a20230531d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Inference Based on Kernel Distribution Function Estimators /$fby Rizky Reza Fauzi, Yoshihiko Maesono 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (103 pages) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$aPrint version: Fauzi, Rizky Reza Statistical Inference Based on Kernel Distribution Function Estimators Singapore : Springer Singapore Pte. Limited,c2023 9789819918614 327 $aKernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator. 330 $aThis book presents a study of statistical inferences based on the kernel-type estimators of distribution functions. The inferences involve matters such as quantile estimation, nonparametric tests, and mean residual life expectation, to name just some. Convergence rates for the kernel estimators of density functions are slower than ordinary parametric estimators, which have root-n consistency. If the appropriate kernel function is used, the kernel estimators of the distribution functions recover the root-n consistency, and the inferences based on kernel distribution estimators have root-n consistency. Further, the kernel-type estimator produces smooth estimation results. The estimators based on the empirical distribution function have discrete distribution, and the normal approximation cannot be improved?that is, the validity of the Edgeworth expansion cannot be proved. If the support of the population density function is bounded, there is a boundary problem, namely the estimator does not have consistency near the boundary. The book also contains a study of the mean squared errors of the estimators and the Edgeworth expansion for quantile estimators. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aNonparametric statistics 606 $aMathematical statistics 606 $aStatistical Theory and Methods 606 $aApplied Statistics 606 $aNon-parametric Inference 606 $aMathematical Statistics 606 $aEstadística matemàtica$2thub 606 $aFuncions de Kernel$2thub 608 $aLlibres electrònics$2thub 610 $aMathematics 615 0$aStatistics. 615 0$aNonparametric statistics. 615 0$aMathematical statistics. 615 14$aStatistical Theory and Methods. 615 24$aApplied Statistics. 615 24$aNon-parametric Inference. 615 24$aMathematical Statistics. 615 7$aEstadística matemàtica 615 7$aFuncions de Kernel 676 $a519.5 700 $aFauzi$b Rizky Reza$01363657 701 $aMaesono$b Yoshihiko$01363658 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910728952603321 996 $aStatistical Inference Based on Kernel Distribution Function Estimators$93384500 997 $aUNINA