Improving the quality of disaggregated SDG indicators with cluster information for small area estimates

2020 ◽  
Vol 36 (4) ◽  
pp. 955-961
Author(s):  
Rizky Zulkarnain ◽  
Dwi Jayanti ◽  
Tri Listianingrum
Keyword(s):  
Small Area ◽  
Research University ◽  
Direct Method ◽  
Random Effect ◽  
Auxiliary Information ◽  
District Level ◽  
Small Areas ◽  
Associate Dean ◽  
Development Goals

The increasing needs for more disaggregated data motivates National Statistical Offices (NSOs) to develop efficient methods for producing official statistics without compromising on quality. In Indonesia, regional autonomy requires that Sustainable Development Goals (SDGs) indicators are available up to the district level. However, several surveys such as the Indonesian Demographic and Health Survey produce estimates up to the provincial level only. This generates gaps in support for district level policies. Small area estimation (SAE) techniques are often considered as alternatives for overcoming this issue. SAE enables more reliable estimation of the small areas by utilizing auxiliary information from other sources. However, the standard SAE approach has limitations in estimating non-sampled areas. This paper introduces an approach to estimating the non-sampled area random effect by utilizing cluster information. This model is demonstrated via the estimation of contraception prevalence rates at district levels in North Sumatera province. The results showed that small area estimates considering cluster information (SAE-cluster) produce more precise estimates than the direct method. The SAE-cluster approach revises the direct estimates upward or downward. This approach has important implications for improving the quality of disaggregated SDGs indicators without increasing cost. The paper was prepared under the kind mentorship of Professor James J. Cochran, Associate Dean for Research, Prof. of Statistics and Operations Research, University of Alabama.

scholarly journals Estimating poverty in the Italian provinces using small area estimation models

2007 ◽  
Vol 4 (1) ◽  
Author(s):  
Claudio Quintano ◽  
Rosalia Castellano ◽  
Gennaro Punzo
Keyword(s):  
Small Area ◽  
Random Effect ◽  
National Level ◽  
Random Effect Model ◽  
Auxiliary Information ◽  
Sample Survey ◽  
Income Poverty ◽  
Estimation Process ◽  
Domain Specific ◽  
Italian Provinces

Sample survey data are broadly used to provide direct estimates of poverty for the whole population and large areas or domains. That is one of the main deficiencies of poverty analysis at a sub-national level (i.e., related either to regions, or provinces). As they are considered very small geographical areas, since the domain-specific sample is not large enough to support direct estimates of adequate precision, they are likely to produce large standard errors, due to the unduly small size of the sample in that area (Ghosh & Rao, 1994). The aim of our paper is to improve the estimation process quality, in terms of efficiency, of some poverty measures for Italian provinces (NUTS3). The adopted approach deals with Area Level Random Effect Model (Fay & Herriot, 1979) which relates small area direct estimators to domain specific covariates, considering the random area effects as independent. Under that model, the Empirical Best Linear Unbiased Predictor (EBLUP) is obtained. We extend the analysis beyond the conventional measures of income poverty that simply dichotomise the population into the "poor" and the "non poor" by a threshold value and we also consider a fuzzy monetary measure treating poverty as a matter of degree (Cheli & Lemmi, 1995; Cheli, 1995). Through such an analysis, we determine some of the socio-economic factors contributing to poverty levels and living standards, and we investigate in depth the territorial perspective. In order to evaluate the performance of the estimation process through small area models and, consequently, the contribution of auxiliary information to composite poverty estimates, we have defined some outcome measures and some quality indicators (Rao, 2003) have been computed. They allow us to test the extent to which the modelling modifies the input direct estimates and the degree of improvement in the accuracy level of the estimates provided by modelling and, more generally, to evaluate the performance of small area estimators.

scholarly journals Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253375
Author(s):  
Joseph Ouma ◽  
Caroline Jeffery ◽  
Colletar Anna Awor ◽  
Allan Muruta ◽  
Joshua Musinguzi ◽  
...  
Keyword(s):  
Small Area ◽  
Auxiliary Information ◽  
Hiv Prevalence ◽  
District Level ◽  
Estimation Methods ◽  
Unit Level ◽  
Model Based ◽  
Prevalence Estimates ◽  
Level Model ◽  
Survey Estimates

Background Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda. Methods Our analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively. Results Estimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates. Conclusions Although the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

2011 ◽  
Vol 41 (6) ◽  
pp. 1189-1201 ◽  
Author(s):  
Michael E. Goerndt ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Estimation Methods ◽  
Stem Volume ◽  
Small Areas ◽  
Forest Density ◽  
Area Estimation ◽  
Forest Attributes ◽  
Areas Of Interest

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.

Small-area estimates using model-based methods

1996 ◽  
Vol 26 (5) ◽  
pp. 758-766 ◽  
Author(s):  
Annika Kangas
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Parametric Models ◽  
Estimation Methods ◽  
Small Areas ◽  
Area Estimation ◽  
Numerical Example ◽  
Model Based

In small areas, the number of sample plots is usually small, and the classical estimators have a large variance. Information from nearby areas can be utilized to improve the subarea estimates using either nonparametric or parametric models. In this study, a number of model-based estimators for small-area estimation are presented. To illustrate the presented methods a numerical example in a real inventory situation is given. The auxiliary information used in this study is pure coordinate information, but the methods are applicable also for other kinds of auxiliary information. The object of this study is to compare the features of the presented small-area estimation methods and to discuss the applicability of these methods in different situations.

scholarly journals Small area quantile estimation based on distribution function using linear mixed models

2021 ◽  
Vol 7 (2) ◽  
pp. 97-114
Author(s):  
Tomasz Stachurski
Keyword(s):  
Distribution Function ◽  
Simulation Study ◽  
Mixed Models ◽  
Small Area ◽  
Linear Mixed Models ◽  
Auxiliary Information ◽  
Quantile Estimation ◽  
Study Variable ◽  
Small Areas ◽  
Economic Studies

In economic studies researchers are often interested in the estimation of the distribution function or certain functions of the distribution function such as quantiles. This work focuses on the estimation quantiles as inverses of the estimates of the distribution function in the presence of auxiliary information that is correlated with the study variable. In the paper a plug-in estimator of the distribution function is proposed which is used to obtain quantiles in the population and in the small areas. Performance of the proposed method is compared with other estimators of the distribution function and quantiles using the simulation study. The obtained results show that the proposed method usually has smaller relative biases and relative RMSE comparing to other methods of obtaining quantiles based on inverting the distribution function.

scholarly journals KAJIAN PENGARUH PENAMBAHAN INFORMASI GEROMBOL TERHADAP PREDIKSI AREA NIRCONTOH PADA DATA BINOMIAL

2020 ◽  
Vol 4 (4) ◽  
pp. 566-578
Author(s):  
Beny Trianjaya ◽  
Anang Kurnia ◽  
Agus M Soleh
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Fixed Effect ◽  
District Level ◽  
Calibration Model ◽  
Labor Conditions ◽  
Sample Area ◽  
Employment Data

Employment data is one of the important indicators related to the development progress of a country. Labor conditions in the territory of Indonesia can only be compared between times through the Survei Angkatan Kerja Nasional (Sakernas) data. Data generated from Sakernas and published by BPS is the number of employed and unemployed. The obstacle in estimating the semester unemployment rate at the regency/municipality level is the lack of a number of examples. One of the indirect estimates currently developing is small area estimation (SAE). This study developed the generalized linear mixed model (GLMM) by adding cluster information and examines the development of modifications with several model scenarios. The purpose of this study was to develop a prediction model for basic GLMM on a small area approach by adding cluster information as a fixed effect or random effect. The simulation results show that Model-2, a model that adds a fixed effect k-cluster and also adds a mean from the estimated effect of random areas in the sample area, is the best model with the smallest relative bias (RB) and Relative root mean squares error (RRMSE). This model is better than the basic GLMM model (Model-0) and Model-1 (a model which only adds a mean from the estimated random effect area in the sample area). Model-2 is applied to estimate the proportion of unemployed sub-district level in Southeast Sulawesi Province. Estimating the proportion of unemployed with calibration Model-2 produced an estimated aggregation of the unemployment proportion of Southeast Sulawesi Province at 0.0272. These results are similar to BPS (0.0272). Thus, the results of the estimated proportion of unemployment at the sub-district level with a calibration Model-2 can be said to be feasible to use.

A Political Economy Model of a Research University

2009 ◽  
pp. 132-143
Author(s):  
K. Sonin ◽  
I. Khovanskaya
Keyword(s):  
Organizational Structure ◽  
Information Structure ◽  
Research University ◽  
Hiring Decisions ◽  
Voting Rules ◽  
Economy Model ◽  
Trade Offs ◽  
Political Economic ◽  
Optimal Incentives

Hiring decisions are typically made by committees members of which have different capacity to estimate the quality of candidates. Organizational structure and voting rules in the committees determine the incentives and strategies of applicants; thus, construction of a modern university requires a political structure that provides committee members and applicants with optimal incentives. The existing political-economic model of informative voting typically lacks any degree of variance in the organizational structure, while political-economic models of organization typically assume a parsimonious information structure. In this paper, we propose a simple framework to analyze trade-offs in optimal subdivision of universities into departments and subdepartments, and allocation of political power.

PERAN WARGA PEDULI AIDS CAHAYA CARE TUREN DALAM MENINGKATKAN KUALITAS HIDUP ODHA

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Tri Nurhudi Sasono
Keyword(s):  
Quality Of Life ◽  
Antiretroviral Drugs ◽  
P Value ◽  
People Living With Hiv ◽  
Purposive Sampling ◽  
Significant Difference ◽  
Development Goals ◽  
Living With Hiv ◽  
Hiv Aids

Abstract : Indicator of the health welfare through Sustanable Development Goals (SDGs) is to reduce the incidence of HIV-AIDS, decrease the rate of the epidemic and maintain the quality of life of people living with HIV-AIDS (PLWHA). Trend cases of HIV-AIDS is the most recent spread among people, especially housewives. In Malang until 2015 found 278 Housewife of 409 cases of AIDS. The prevalence of HIV-AIDS in Malang Regency is ranked second after Surabaya city in East Java. For the importance of public participation and citizen care AIDS Cahaya Care Turen take responsibility for the condition. Determination Rule Goverment number 2 2015 year on the Participation of the community response to HIV-AIDS in Malang as a legal rule. Concerned Citizens activities AIDS (WPA). WPA Cahaya Care Turen is increases HIV risk and quality of life PLWHA. The purpose of this study was to determine the role of Citizens AIDS Cahaya Care Quality of Care Turen against people living with HIV in Puskesmas Turen Malang. The study design using a quasi-experimental, with purposive sampling using a sampling technique. Total number of research subjects 23. Based on test results obtained with the Wilcoxon p value <0.005, which means that there is a significant difference before and after PLWHA joining participated in the WPA Cahaya Care Turen. The conclusion of this study is WPA activities involving people living with HIV and at risk groups can optimize compliance with antiretroviral drugs that have an impact on improving the quality of life of PLHIV. Suggestions in this research is done WPA Program activities are structured and ongoing cross-sector in order to improve the quality of life and empower PLWHA.Keywords : WPA Cahaya Care Turen, Quality of life, PLWHA Abstrak : Salah satu indikator kesejahteraan kesehatan melalui Sustanable Development Goals (SDGs) adalah menekan angka kejadian HIV-AIDS, menurunkan laju epidemik dan mempertahankan kualitas hidup Orang dengan HIV-AIDS (ODHA). Trend kasus HIV-AIDS terkini terbanyak adalah menjangkit dikalangan masyarakat khususnya pada ibu rumah tangga. Kabupaten Malang sampai dengan tahun 2015 ditemukan 278 Ibu Rumah Tangga dari 409 kasus AIDS. Prevalensi HIV-AIDS di Kabupaten Malang ini merupakan peringkat kedua di Jawa Timur setelah Kota Surabaya. Untuk itu pentingnya peran serta masyarakat dan warga peduli AIDS Cahaya Care Turen ikut bertanggung jawab terhadap kondisi tersebut. Penetapan Peraturan Bupati Malang no.2 th.2015 tentang Peran serta masyarakat penanggulangan HIV-AIDS di Kabupaten Malang diharapkan dapat mengurangi risiko penularan HIV dan meningkatkan kualitas hidup ODHA. Tujuan dari penelitian ini adalah untuk mengetahui Peran Warga Peduli AIDS Cahaya Care Turen terhadap Kualitas ODHA Di Wilayah Kerja Puskesmas Turen Kabupaten Malang. Desain penelitian menggunakan quasi eksperimen, dengan teknik sampling menggunakan purposive sampling. Jumlah subyek penelitian sejumlah 23. Berdasarkan hasil uji dengan Wilcoxon didapatkan nilai p value < 0.005 yang berarti bahwa terdapat perbedaan bermakna sebelum dan sesudah ODHA bergabung mengikuti kegiatan WPA Cahaya Care Turen. Kesimpulan dalam penelitian ini adalah kegiatan WPA dengan melibatkan ODHA dan kelompok beresiko dapat mengoptimalkan kepatuhan obat ART sehingga berdampak terhadap peningkatan kualitas hidup ODHA. Saran dalam penelitian ini adalah dilakukannya Program kegiatan WPA yang terstruktur dan berkesinambungan lintas sektor guna meningkatkan kualitas hidup dan memberdayakan ODHA.     Kata kunci : WPA Cahaya Care Turen, kualitas hidup, ODHA

Mindfulness- and Acceptance-Based Interventions for Stroke Survivors: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 003435522110432
Author(s):  
Areum Han
Keyword(s):  
Systematic Review ◽  
Depressive Symptoms ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Mental Fatigue ◽  
Stroke Survivors ◽  
Positive Effects ◽  
Meta Analyses

Objective: Mindfulness- and acceptance-based intervention (MABI) is an emerging evidenced-based practice, but no systematic review incorporating meta-analyses for MABIs in stroke survivors has been conducted. The objective of this systematic review was to measure the effectiveness of MABIs on outcomes in people with stroke. Method: Three electronic databases, including PubMed, CINAHL, and PsycINFO, were searched to identify relevant studies published in peer-reviewed journals. The methodological quality of the included studies was assessed. Data were extracted and combined in a meta-analysis with a random-effect model to compute the size of the intervention effect. Results: A total of 11 studies met the eligibility criteria. Meta-analyses found a small-to-moderate effect of MABIs on depressive symptoms (standardized mean difference [SMD] = 0.39, 95% confidence interval [CI] = [0.12, 0.66]) and a large effect on mental fatigue (SMD = 1.22, 95% CI = [0.57, 1.87]). No statistically significant effect of MABIs on anxiety, quality of life, and mindfulness was found, but there was a trend in favor of MABIs overall. Conclusions: This meta-analysis found positive effects of MABIs on depressive symptoms and mental fatigue in stroke survivors, but future high-quality studies are needed to guarantee treatment effects of MABIs on varied outcomes in stroke survivors.

scholarly journals Representing “University Education” in the Horizon of the Concept of Sustainable Development and Security Discourse

2021 ◽  
Vol 295 ◽  
pp. 05003
Author(s):  
Konstantin Maltsev ◽  
Larisa Binkovskaya ◽  
Anni Maltseva
Keyword(s):  
Sustainable Development ◽  
Human Capital ◽  
Sustainable Development Goals ◽  
Global Perspective ◽  
University Education ◽  
Economic Paradigm ◽  
Security Discourse ◽  
Development Goals ◽  
The University

The relevance of linking the concept of sustainable development and the security discourse reveals the possibility of believing that education is a prerequisite for ensuring that “sustainable development” goals become a reality. The university has a twofold task: first, to produce knowledge that meets the demands of our time, i.e. technical knowledge, and second, to form human capital, to train specialists capable of the practical application of instrumental knowledge. The initial orientation of the concept of “sustainable development” towards a global perspective: the representation of reality in an economic paradigm, i.e., totally determined by the “logic of capital”, “monocausal economic logic”, determines the criteria by which the quality of human capital, its price, and efficiency of production of a standardized product are evaluated, the production of which is undertaken by the university-corporation that has replaced the classical “university of reason”, whose ontic foundations - the “Hegelian science”, the romantic “education of humanity” - are no longer valid in what is called modernity. The article demonstrates how modernity, constituted concerning a certain self-representation of the New European subject and presented in the liberal economic paradigm, predetermines both the goal-setting in determined by its representation of the development and the content and methods of the reform of the university. It is concluded that “sustainable development”, “security” and “university-corporation” are essentially connected with the representation of reality in the liberal version of the economic paradigm.

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