A unified Monte-Carlo jackknife for small area estimation after model selection

被引：10

作者：

Jiang, Jiming ^{[1
]}

Lahiri, P. ^{[2
]}

Thuan Nguyen ^{[3
]}

机构：

[1] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA

[2] Joint Program Survey Methodol, 1218 LeFrak Hall,7251 Preinkert Dr, College Pk, MD 20742 USA

[3] Sch Publ Hlth, 840 SW Gaines St, Portland, OR 97239 USA

来源：

ANNALS OF MATHEMATICAL SCIENCES AND APPLICATIONS | 2018年 / 3卷 / 02期

关键词：

Computer intensive; jackknife; log-MSPE; measure of uncertainty; model selection; Monte-Carlo; second-order unbiasedness; small area estimation;

D O I：

10.4310/AMSA.2018.v3.n2.a2

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We consider estimation of measure of uncertainty in small area estimation (SAE) when a procedure of model selection is involved prior to the estimation. A unified Monte-Carlo jackknife method, called McJack, is proposed for estimating the logarithm of the mean squared prediction error. We prove the second-order unbiasedness of McJack, and demonstrate the performance of McJack in assessing uncertainty in SAE after model selection through empirical investigations that include simulation studies and real-data analyses.

引用

页码：405 / +

页数：38

共 50 条

[31] SENSITIVITY AND UNCERTAINTY OF PROCESS DESIGNS TO THERMODYNAMIC MODEL PARAMETERS - A MONTE-CARLO APPROACH
REED, ME
WHITING, WB
CHEMICAL ENGINEERING COMMUNICATIONS, 1993, 124 : 39 - 48
[32] Verification for dose estimation performance of a Monte-Carlo based treatment planning system in University of Tsukuba
Kumada, Hiroaki
Takada, Kenta
Aihara, Teruhito
Matsumura, Akira
Sakurai, Hideyuki
Sakae, Takeji
APPLIED RADIATION AND ISOTOPES, 2020, 166
[33] Uncertainty quantification analysis and statistical estimation for LBLOCA in a PWR using Monte-Carlo and alternative methods
Kang, Dong Gu
ANNALS OF NUCLEAR ENERGY, 2021, 150
[34] Comparison of different Monte-Carlo based approaches to calculating the system matrix for small animal PET
Gimenez, E. N.
Mora, C.
Gimenez, M.
Benlloch, J. M.
Rafecas, M.
2005 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOLS 1-5, 2005, : 2085 - 2088
[35] Monte Carlo Markov chain methods and model selection in genetic epidemiology
Wijsman, EM
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2000, 32 (3-4) : 349 - 360
[36] A Monte Carlo Simulation Study on Model Selection in Latent Markov Models
Gungor, Duygu
Ulbe, Selva
Bas, Samet
TURK PSIKOLOJI DERGISI, 2019, 34 (83): : 94 - 108
[37] A dynamic compartment model for spatially heterogeneous reactors: Scalar and Monte-Carlo particle mixing
Morchain, Jerome
Mayorga, Carlos
Villedieu, Philippe
Line, Alain
CHEMICAL ENGINEERING RESEARCH & DESIGN, 2024, 205 : 628 - 639
[38] Application study of a correction method for a spacecraft thermal model with a Monte-Carlo hybrid algorithm
Cheng WenLong
Liu Na
Li Zhi
Zhong Qi
Wang AiMing
Zhang ZhiMin
He ZongBo
CHINESE SCIENCE BULLETIN, 2011, 56 (13): : 1407 - 1412
[39] MONTE-CARLO SIMULATION OF MULTICOMPONENT SYSTEM BY MACRO STATE MARKOV-CHAIN MODEL
CHEN, A
HIRTZEL, CS
SEPARATIONS TECHNOLOGY, 1994, 4 (03): : 167 - 173
[40] Using a Monte-Carlo model to identify best filler arrangement in thermally conductive materials
Descamps, P.
Van Wassenhove, G.
Teixeira, S.
Beaucarne, G.
MICROELECTRONICS JOURNAL, 2015, 46 (12) : 1179 - 1184

← 1 2 3 4 5 →