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 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 Simulation Tools for Small Area Estimation: Introducing the R-package saeSim

2016 ◽  
Vol 45 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Sebastian Warnholz ◽  
Timo Schmid
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
R Package ◽  
Research Process ◽  
Simulation Studies ◽  
Simulation Tools ◽  
Area Estimation ◽  
Regional Estimates ◽  
Special Case

The demand for reliable regional estimates from sample surveys has been substantially grown over the last decades. Small area estimation provides statistical methods to produce reliable predictions when the sample sizes in regions are too small to apply direct estimators. Model- and design-based simulations are used to gain insights into the quality of the introduced methods. In this article we present a framework which may help to guarantee the reproducibility of simulation studies in articles and during research. The introduced R-package saeSim is adjusted to provide a simulation environment for the special case of small area estimation. The package may allow the prospective researcher during the research process to produce simulation studies with a minimal eort of coding.

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

Potential for Community Relocation among Residents of Alberta's Psychiatric Facilities: A Needs Assessment

1997 ◽  
Vol 42 (7) ◽  
pp. 750-757 ◽  
Author(s):  
Heather L Holley ◽  
Betty Jeffers ◽  
Phyllis Hodges
Keyword(s):  
Quality Of Life ◽  
Substance Abuse ◽  
Forensic Psychiatry ◽  
Psychiatric Hospitals ◽  
Mental Illnesses ◽  
Improve Quality ◽  
Short Term ◽  
Community Based ◽  
Institutionalized Patients

Objective: To identify the proportion of residents in Alberta's psychiatric hospitals who would be relocatable to community-based alternatives to care if appropriate programs were made available. Methods: A survey of a representative sample of residents in all psychiatric hospitals and care centres was undertaken, excluding patients in specialized programs for forensic psychiatry, brain injury, and substance abuse. Results: Findings indicate that 11% of institutionalized patients could be considered good candidates for relocation to community-based alternative care. A further 35% were considered to have potential for relocation provided an intensive and complex array of services was first made available. Conclusion: Results support a phased-in approach to hospital downsizing and the need for considerable transitional funding. Because hospital downsizing is unlikely to save dollars, at least in the short term, reforms must remain guided by humanitarian motivations to improve quality of life for persons with severe and persistent mental illnesses.

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.

Health-related quality of life among adults 65 years and older in the United States, 2011–2012: a multilevel small area estimation approach

2017 ◽  
Vol 27 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Yu-Hsiu Lin ◽  
Alexander C. McLain ◽  
Janice C. Probst ◽  
Kevin J. Bennett ◽  
Zaina P. Qureshi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
United States ◽  
Small Area ◽  
Small Area Estimation ◽  
The United States ◽  
Health Related Quality ◽  
Area Estimation ◽  
Related Quality ◽  
Health Related
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