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Model-based small area estimation with no samples within the areas,by benchmarking to marginal cross-sectional and time-series estimates

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Statistical Theory and Related Fields 2020年第1期4卷 28-42页

作者： Danny Pfeffermann Michael Sverchkov Richard Tiller Lizhi Liu National Statistician and CBS Director JerusalemIsrael Department of Statistics Hebrew University of JerusalemJerusalemIsrael Southampton Statistical Sciences Research Institute University of SouthamptonSouthamptonUK Bureau of Labor Statistics WashingtonDCUSA

Official monthly *** force estimation at the sub-State level(mostly counties)is based on what is known as the‘Handbook’(HB)method,one of the earliest uses of administrative data for small area *** administrative data,however,are poor in coverage and have conceptual *** attempts to correct for the resulting bias of the HB estimates by informal(implicit)modelling have not been successful,due to the absence of regular direct monthly survey estimates at the sub-State *** the sub-State HB estimates each month to the State model dependent estimates helps to correct for an overall bias,but not in individual *** this article we propose benchmarking additionally to the annual model-dependent area *** annual models include known administrative data as covariates,and are used to define corresponding monthly sub-State models,which in turn enable producing monthly synthetic estimates as possible substitutes for the HB estimates in real time *** estimates,which account for sampling errors and the errors of the model dependent estimators are *** for sub-State areas in the State of Arizona are used for *** the methodology developed in this article stems from a particular(but very important)application,it is general and applicable to other similar problems.

关键词： Benchmarking Denton method mixed models variance estimation

Multimodel inference based on smoothed information criteria

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Science China Mathematics 2021年第11期64卷 2563-2578页

作者： Shangwei Zhao Xinyu Zhang College of Science Minzu University of ChinaBeijing 100086China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

The multimodel inference makes statistical inferences from a set of plausible models rather than from a single *** this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs(see Buckland et al.(1997)and Burnham and Anderson(2003)),which are termed as smoothed Akaike information criterion(SAIC)and smoothed Bayesian information criterion(SBIC)*** to their simplicity and applicability,these methods are very widely used in many *** using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is ***,we propose a simulation-based inference for SAIC based on the limiting *** the simulation study and the real data example show the promising performance of the proposed simulationbased inference.

关键词： information criterion model averaging multimodel inference variance estimation weight

A New Class of L-Moments Based Calibration variance Estimators

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Computers, Materials & Continua 2021年第3期66卷 3013-3028页

作者： Usman Shahzad Ishfaq Ahmad Ibrahim Mufrah Almanjahie Nadia H.Al Noor Muhammad Hanif Department of Mathematics and Statistics International Islamic UniversityIslamabad46000Pakistan Department of Mathematics and Statistics PMAS-Arid Agriculture UniversityRawalpindi46300Pakistan Department of Mathematics King Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Department of Mathematics College of ScienceMustansiriyah UniversityBaghdad10011Iraq

variance is one of themost important measures of descriptive statistics and commonly used for statistical *** traditional second-order central moment based variance estimation is a widely utilized ***,traditional variance estimator is highly affected in the presence of extreme *** this paper initially,proposes two classes of calibration estimators based on an adaptation of the estimators recently proposed by Koyuncu and then presents a new class of L-Moments based calibration variance estimators utilizing L-Moments characteristics(L-location,Lscale,L-CV)and auxiliary *** is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than adapted *** data is considered for assessing the performance of the proposed *** also demonstrated an application related to apple fruit for purposes of the *** artificial and real data sets,percentage relative efficiency(PRE)of the proposed class of estimators with respect to adapted ones are *** PRE results indicate to the superiority of the proposed class over adapted ones in the presence of extreme *** this manner,the proposed class of estimators could be applied over an expansive range of survey sampling whenever auxiliary information is available in the presence of extreme values.

关键词： L-moments variance estimation calibration approach stratified random sampling

L-Moments Based Calibrated variance Estimators Using Double Stratified Sampling

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Computers, Materials & Continua 2021年第9期68卷 3411-3430页

作者： Usman Shahzad Ishfaq Ahmad Ibrahim Mufrah Almanjahie Nadia H.Al–Noor Department of Mathematics and Statistics International Islamic UniversityIslamabad44000Pakistan Department of Mathematics and Statistics PMAS-Arid Agriculture UniversityRawalpindi46300Pakistan Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Department of Mathematics College of ScienceMustansiriyah UniversityBaghdad10011Iraq

variance is one of the most vital measures of dispersion widely employed in practical aspects.A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values,and thus its results cannot be relied *** momentum from Koyuncu’s recent work,the present paper focuses first on proposing two classes of variance estimators based on linear moments(L-moments),and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments(L-location,L-cv,L-variance).Three populations are taken into account to assess the efficiency of the new *** first and second populations are concerned with artificial data,and the third populations is concerned with real *** percentage relative efficiency of the proposed estimators over existing ones is *** the presence of extreme values,our findings depict the superiority and high efficiency of the proposed classes over traditional ***,when auxiliary data is available along with extreme values,the proposed classes of estimators may be implemented in an extensive variety of sampling surveys.

关键词： variance estimation L-moments calibration approach double sampling stratified random sampling

On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method

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Communications in Computational Physics 2014年第7期16卷 467-490页

作者： Jun Li Philippe Vignal Shuyu Sun Victor M.Calo Center for Numerical Porous Media King Abdullah University of Science and TechnologyThuwalSaudi Arabia Material Science and Engineering King Abdullah University of Science and TechnologyThuwalSaudi Arabia AppliedMathematics and Computational Science Earth Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia.

InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated *** samples are selected in accordance with the probability distribution function,known from the partition function of equilibrium *** the stochastic error of the simulation results is significant,it is desirable to understand the variance of the estimation by ensemble average,which depends on the sample size(i.e.,the total number of samples in the set)and the sampling interval(i.e.,cycle number between two consecutive samples).Although large sample sizes reduce the variance,they increase the computational cost of the *** a given CPU time,the sample size can be reduced greatly by increasing the sampling interval,while having the corresponding increase in variance be negligible if the original sampling interval is very *** this work,we report a few general rules that relate the variance with the sample size and the sampling *** results are observed and confirmed *** variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo *** main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.

关键词： Phase coexistence Gibbs ensemble molecular simulation Markov Chain Monte Carlo method variance estimation blocking method

Sampling Error estimation in Stratified Surveys

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Open Journal of Statistics 2013年第3期3卷 200-212页

作者： Ricardo Cao Jose A.Vilar Juan M.Vilar Ana K.Lopez Department of Mathematics Campus de ElvinaUniversity of A CorunaA CorunaSpain

Many operations carried out by official statistical institutes use large-scale surveys obtained by stratified random sampling without replacement. Variables commonly examined in this type of surveys are binary, categorical and continuous, and hence, the estimates of interest involve estimates of proportions, totals and means. The problem of approximating the sampling relative error of this kind of estimates is studied in this paper. Some new jackknife methods are proposed and compared with plug-in and bootstrap methods. An extensive simulation study is carried out to compare the behavior of all the methods considered in this paper.

关键词： variance estimation Jackknife Bootstrap Stratified Sampling

Robust Adaptive Kalman Filtering For Target Tracking With Unknown Observation Noise

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Robust Adaptive Kalman Filtering For Target Tracking With Un...

Blind Equalization under Noisy Environment using Bias-compensated RLS method

第24届中国控制与决策会议

作者： Yongchen Li, Jianxun Li Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240

The Kalman filter （KF） is widely used in the field of target tracking. In practical target tracking systems through, the observation noise is often unknown and characterized by heavier tails named outliers. That will affect the performance of target tracking seriously and even lead to filtering divergence. To overcome this problem, a novel robust Kalman filter （RKF） is proposed based on the maximum a posteriori （MAP） estimation to observation outliers. In addition, the adaptive estimate of observation noise variance is also given based on the weighted correlation innovation （WCI） sequences of output of a steady state Kalman filter （SSKF）. Finally, a robust adaptive Kalman filter （RAKF） algorithm is raised by implementing RKF and adaptive estimate of simultaneously. The feasibility of the algorithm is demonstrated by an example of target tracking with simulation.

关键词： Kalman filter outlier robustness variance estimation adaptability target tracking

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Blind Equalization under Noisy Environment using Bias-compen...