Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique

Chandra, Hukum; Salvati, Nicola; Sud, U. C.

doi:10.1080/02664763.2011.559202

Cited by 36 publications

(69 citation statements)

References 10 publications

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“…For this reason, we present the methodological framework used to derive local or small area estimates of the prevalence and dynamics of disadvantage based on the area‐level version of the generalised linear mixed model with logit link function (i.e. logistic‐normal mixed model), suitable for modelling discrete data, specifically through the binomial distribution (Chandra et al , ; Chandra et al , ; Amoako Johnson et al , ). This provides reliable model‐based estimates at the regional level and their standard errors.…”

Section: Methodsmentioning

confidence: 99%

“…These estimates are computed to assess the reliability of the small area estimates and also to construct confidence intervals for the small area parameters of interest. Following Rao and Molina (, Chapter 5), Saei and Chambers (2003) and Chandra et al (), the MSE estimate of under model is given by

mse ((), {\overset{p}{true}}_{r}^{EP}) = M_{1} ((), {\overset{σ}{true}}_{u}^{2}) + M_{2} ((), {\overset{σ}{true}}_{u}^{2}) + {2 M}_{3} false({\overset{σ}{true}}_{u}^{2} false) .

…”

Section: Methodsmentioning

confidence: 99%

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Localised Estimates of Dynamics of Multi‐dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data

Baffour

Chandra

Martínez³

2018

Int Statistical Rev

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Deep and persistent disadvantage is an important, but statistically rare, phenomenon in the population, and sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. Survey samples are typically designed to produce estimates of population characteristics of planned areas. The sample sizes are calculated so that the survey estimator for each of the planned areas is of a desired level of precision. However, in many instances, estimators are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This has led to the development of a class of indirect estimators that make use of information from related areas through modelling. A model is used to link similar areas to enhance the estimation of unplanned areas; in other words, they borrow strength from the other areas. Doing so improves the precision of estimated characteristics in the small area, especially in areas with smaller sample sizes. Social science researchers have increasingly employed small area estimation to provide localised estimates of population characteristics from surveys. We explore how to extend this approach within the context of deep and persistent disadvantage in Australia. We find that because of the unique circumstances of the Australian population distribution, direct estimates of disadvantage have substantial variation, but by applying small area estimation, there are significant improvements in precision of estimates.

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Section: Methodsmentioning

confidence: 99%

mse ((), {\overset{p}{true}}_{r}^{EP}) = M_{1} ((), {\overset{σ}{true}}_{u}^{2}) + M_{2} ((), {\overset{σ}{true}}_{u}^{2}) + {2 M}_{3} false({\overset{σ}{true}}_{u}^{2} false) .

…”

Section: Methodsmentioning

confidence: 99%

Localised Estimates of Dynamics of Multi‐dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data

Baffour

Chandra

Martínez³

2018

Int Statistical Rev

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“…The estimates of fixed effect parameter

\hat{boldλ}

and random effects

{\hat{a}}_{i}

are obtained by an iterative procedure that combines the PQL estimation of λ and

a = {(a_{1}, \dots, a_{D})}^{T}

with restricted maximum likelihood (REML) estimation of

σ_{a}^{2}

(see Chandra et al., ; Johnson, Chandra, Brown, & Padmadas, ; and references therein for further details). The direct estimate of proportions and area‐level auxiliary variables

({\overset{X}{true}}_{i}; i = 1, \dots, D false)

can also be modeled by Fay and Herriot () model and then the EBLUP of small area proportions (i.e., EBLUP.FH) can be obtained.…”

Section: Small Area Estimation For Proportionsmentioning

confidence: 99%

“…We consider two commonly used measures namely the percent coefficient of variation (CV) and the 95% CIs for small area estimates diagnostics (Chandra et al., 2011; Johnson et al., ). We compute the percent CV to assess the improved precision of the EPP1 estimates compared to the DIR estimates.…”

Section: Application To Real Datamentioning

confidence: 99%

“…As a result, in many small area applications, this EPP estimator cannot be used. If aggregate‐level population information for the auxiliary variables is available, the EPP under an area‐level GLMM can be applied (Chandra, Salvati & Sud, ). Recently, Boubeta, Lombardìa and Morales () described empirical best prediction under area‐level Poisson mixed models.…”

Section: Introductionmentioning

confidence: 99%

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Small area estimation of proportions with different levels of auxiliary data

2018

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Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied. Based on the level of auxiliary information available, different small area predictors for estimation of proportions are proposed. Analytic and bootstrap approaches to estimating the mean squared error of the proposed small area predictors are also developed. Monte Carlo simulations based on both simulated and real data show that the proposed small area predictors work well for generating the small area estimates of proportions and represent a practical alternative to the above approach. The developed predictor is applied to generate estimates of the proportions of indebted farm households at district-level using debt investment survey data from India.

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Understanding the impacts of predecessor rain events on flood hazard in a changing climate

Khatun

Ganguli

Bisht

et al. 2022

Hydrological Processes

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This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by ‘AND’ operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period.

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Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique

Cited by 36 publications

References 10 publications

Localised Estimates of Dynamics of Multi‐dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data

Localised Estimates of Dynamics of Multi‐dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data

Small area estimation of proportions with different levels of auxiliary data

Understanding the impacts of predecessor rain events on flood hazard in a changing climate

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