scholarly journals Small area estimation in the case of nonesponse

2009 ◽  
Vol 50 ◽  
Author(s):  
Vilma Nekrašaitė-Liegė
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Small Area ◽  
Finite Population ◽  
Real Data ◽  
Area Estimation ◽  
Empirical Results ◽  
Different Types ◽  
Business Survey ◽  
Repeated Samples

In this paper the effect of model and nonresponseadjustment on different types of estimators for the totals of small area domains is examined. The empirical results are based on Monte Carlo simulations with repeated samples drawn from a finite population constructed from the real data from the Lithuanian Business Survey.

scholarly journals Weighting and imputation comparison in small area estimation

2010 ◽  
Vol 51 ◽  
Author(s):  
Vilma Nekrašaitė-Liegė
Keyword(s):  
Monte Carlo ◽  
Small Area ◽  
Finite Population ◽  
Improve Quality ◽  
Random Parameters ◽  
Short Term ◽  
Area Estimation ◽  
Domain Level ◽  
Repeated Samples

In this paper, different methods of nonresponse adjustment for the totals of small area domains are examined. To improve quality of estimations linear model with random parameters at domain level is used. The empirical results are based on Monte Carlo simulations with repeated samples drawn from a finite population constructed from the Lithuanian survey on short-term statistics on service.

scholarly journals Bayesian small area estimation for skewed business survey variables

2017 ◽  
Vol 67 (4) ◽  
pp. 861-879
Author(s):  
Enrico Fabrizi ◽  
Maria Rosaria Ferrante ◽  
Carlo Trivisano
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Business Survey

scholarly journals Monte Carlo simulations of two-component drop growth by stochastic coalescence

2008 ◽  
Vol 8 (2) ◽  
pp. 7289-7313 ◽  
Author(s):  
L. Alfonso ◽  
G. B. Raga ◽  
D. Baumgardner
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Stochastic Algorithm ◽  
Aerosol Composition ◽  
Particle Charging ◽  
Two Component ◽  
Different Types ◽  
Framework Species ◽  
Stochastic Coalescence ◽  
Good Agreement

Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method.~The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by comparing the numerical with the analytical solutions found by Lushnikov (1975). Very good agreement was observed between the Monte Carlo simulations and the analytical solution. Simulation results are presented for bi-variate constant and hydrodynamic kernels. The algorithm can be easily extended to incorporate various properties of clouds such as including several crystal habits, different types of soluble CCN, particle charging and drop breakup.

scholarly journals Assessment of knowledge and confidence for E-learning

2019 ◽  
Vol 11 (1) ◽  
pp. 104-115
Author(s):  
Devkan Kaleci ◽  
Ergün Akleman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Multiple Choice ◽  
Learning Objectives ◽  
Choice Test ◽  
Multiple Choice Test ◽  
Actual Knowledge ◽  
Different Types ◽  
E Learning ◽  
The Subject

One of the most important goals in E-learning is to guarantee that participants reach the learning objectives. We have observed that having the knowledge of the subject is not sufficient for reaching learning objectives. The participants must also develop understanding that they know the subject, which we have named confidence. In this work, we have demonstrated that it is possible to assess both knowledge and confidence using only two different types of multiple-choice test questions. We have developed 1) a method to design questions to identify both knowledge and confidence and 2) a method to estimate actual knowledge and confidence from answers. We have evaluated our method using Monte-Carlo simulations. Our simulations demonstrated that it is always possible to obtain reliable estimations for knowledge and confidence using approximately 100 multiple choice test questions in a given subject.

scholarly journals PENDUGAAN ANGKA KEMISKINAN DI KABUPATEN PADANG PARIAMAN MENGGUNAKAN SMALL AREA ESTIMATION DENGAN PENDEKATAN HIERARCHICAL BAYESIAN (HB) LOGLOGISTIK

2019 ◽  
Vol 8 (2) ◽  
pp. 76
Author(s):  
Jusri Repi Basri Yuliani ◽  
Maiyastri Maiyastri ◽  
Rita Diana
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gibbs Sampling ◽  
Standard Error ◽  
Small Area ◽  
Small Area Estimation ◽  
Hierarchical Bayesian ◽  
Area Estimation ◽  
Level Model

Penelitian ini mengkaji tentang pendekatan Hierarchical Bayesian (HB) Loglogistik yang diaplikasikan pada Small Area Estimation (SAE) dengan tujuan mengestimasi tingkat kemiskinan di Kabupaten Padang Pariaman. Metode pendugaan area kecil yang digunakan pada penelitian ini adalah model level area dasar (basic area level model ) dengan bantuan variabel penyerta yang tersedia pada level kecamatan. Variabel penyerta yang digunakan pada penelitian ini yaitu rasio SLTA/Sederajat (X1), persentase keluarga pertanian (X2), rasio industri mikro kecil (X3), persentase buruh tani dalam setiap anggota keluarga (X4), kepadatan penduduk (X5), dan persentase penduduk pelanggan listrik PLN (X6). Bentuk integrasi yang kompleks dari sebaran peluang bersyarat pada model diselesaikan menggunakan Markov Chain Monte Carlo (MCMC) dengan menerapkan algortima Gibbs Sampling dan bantuan software WinBugs 1.4.3. Hasil estimasi menggunkan model HB yang diperoleh dibandingkan dengan hasil estimasi pendugaan langsung dengan memperhatikan nilai standard error sebagai tolok ukurnya. Hasil pendugaan tingkat kemiskinan untuk level kecamatan di Kabupaten Padang Pariaman dengan model HB menunjukkan nilai standard error yang kecil.Kata Kunci: Tingkat kemiskinan, Small Area Estimation, Hierarchical Bayesian

scholarly journals A Study of Small Area Estimation for Italian Structural Business Statistics

2018 ◽  
Vol 34 (2) ◽  
pp. 543-555
Author(s):  
Orietta Luzi ◽  
Fabrizio Solari ◽  
Fabiana Rocci
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Optimal Estimation ◽  
Estimation Strategy ◽  
Area Estimation ◽  
Statistical Institute ◽  
Business Statistics ◽  
Different Types ◽  
Quality And Reliability

Abstract The Frame SBS is a statistical register which has been developed at the Italian National Statistical Institute to support the annual estimation of structural business statistics (SBS). Actually, a number of core SBS are estimated by combining microdata directly supplied by different administrative sources. In this context, more accurate estimates for those SBS that are not covered by administrative sources can be obtained through small area estimation (SAE). In this article, we illustrate an application of SAE methods in the framework of the Frame SBS register in order to assess the potential advantages that can be achieved in terms of increased quality and reliability of the target variables. Different types of auxiliary information and approaches are compared in order to identify the optimal estimation strategy in terms of precision of the estimates.

On the computational estimation of high order GARCH model

2021 ◽  
Vol 8 (4) ◽  
pp. 797-806
Author(s):  
A. Settar ◽  
◽  
N. I. Fatmi ◽  
M. Badaoui ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Monte Carlo Simulations ◽  
Garch Model ◽  
Real Data ◽  
Conditional Variance ◽  
High Order ◽  
Computational Estimation ◽  
Necessary And Sufficient ◽  
The Garch Model

To guarantee the non-negativity of the conditional variance of the GARCH process, it is sufficient to assume the non-negativity of its parameters. This condition was empirically violated besides rendering the GARCH model more restrictive. It was subsequently relaxed for some GARCH orders by necessary and sufficient constraints. In this paper, we generalized an approach for the QML estimation of the GARCH(p,q) parameters for all orders $p\geq 1$ and $q\geq1$ using a constrained Kalman filter. Such an approach allows a relaxed QML estimation of the GARCH without the need to identify and/or apply the relaxed constraints to the parameters. The performance of our method is demonstrated through Monte Carlo simulations and empirical applications to real data.

scholarly journals PENERAPAN MODEL SPATIAL LOGIT-NORMAL PADA SMALL AREA ESTIMATION DENGAN METODE HIERARCHICAL BAYES

2020 ◽  
Vol 2019 (1) ◽  
pp. 59-66
Author(s):  
Taly Purwa
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Small Area ◽  
Small Area Estimation ◽  
Random Effect ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Hierarchical Bayes ◽  
Area Estimation ◽  
Spatial Random Effect

Penelitian ini menerapkan model Spatial Logit-normal pada Small Area Estimation (SAE) untuk estimasi proporsi penduduk dengan asupan kalori minimum di bawah 1.400 kkal/kapita/hari pada level kecamatan di Provinsi Bali Tahun 2014 yang merupakan indikator 2.1.2(A) pada tujuan ke-2 SDGs dalam rangka mengukur capaian dan mendukung tercapainya target SDGs pada level lebih tinggi. Terdapat tiga model SAE yang digunakan dengan spesifikasi random effect yang berbeda, yaitu model dengan random effect yang bersifat saling bebas (independen), spatial random effect (iCAR) serta model dengan kedua jenis random effect sekaligus (BYM). Penggunaan unsur spatial random effect diharapkan dapat meningkatkan efisiensi hasil estimasi. Metode estimasi menggunakan pendekatan Hierarchical Bayes (HB) dengan metode Markov Chain Monte Carlo (MCMC) algoritma Gibbs Sampling. Estimasi parameter pada ketiga model menunjukkan hasil yang relatif tidak berbeda dimana hanya ada satu variabel prediktor yang memiliki pengaruh signifikan, yaitu proporsi keluarga pertanian, pada model dengan random effect independen dan model BYM. Sedangkan pada model iCAR tidak ada satu pun variabel prediktor yang berpengaruh signifikan. Berdasarkan nilai Deviance Information Criterion (DIC), model terbaik adalah model BYM. Akan tetapi penambahan unsur spatial random effect bersamaan dengan random effect independen tidak secara signifikan dapat meningkatkan efisiensi hasil estimasi akibat dari minimnya nilai dependensi spasial Moran’s I. Secara visual, pemetaan hasil estimasi dengan model terbaik tidak menunjukkan adanya pola persebaran atau pengelompokan tertentu pada level kecamatan.

scholarly journals Small-Area Estimation with Zero-Inflated Data – a Simulation Study

2016 ◽  
Vol 32 (4) ◽  
pp. 963-986 ◽  
Author(s):  
Sabine Krieg ◽  
Harm Jan Boonstra ◽  
Marc Smeets
Keyword(s):  
Simulation Study ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Real Data ◽  
Small Sample ◽  
Artificial Data ◽  
Normal Distributions ◽  
Area Estimation ◽  
Small Sample Sizes

Abstract Many target variables in official statistics follow a semicontinuous distribution with a mixture of zeros and continuously distributed positive values. Such variables are called zero inflated. When reliable estimates for subpopulations with small sample sizes are required, model-based small-area estimators can be used, which improve the accuracy of the estimates by borrowing information from other subpopulations. In this article, three small-area estimators are investigated. The first estimator is the EBLUP, which can be considered the most common small-area estimator and is based on a linear mixed model that assumes normal distributions. Therefore, the EBLUP is model misspecified in the case of zero-inflated variables. The other two small-area estimators are based on a model that takes zero inflation explicitly into account. Both the Bayesian and the frequentist approach are considered. These small-area estimators are compared with each other and with design-based estimation in a simulation study with zero-inflated target variables. Both a simulation with artificial data and a simulation with real data from the Dutch Household Budget Survey are carried out. It is found that the small-area estimators improve the accuracy compared to the design-based estimator. The amount of improvement strongly depends on the properties of the population and the subpopulations of interest.

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