Finite Population Survey Sampling: An Unapologetic Bayesian Perspective.

Abstract read

In one paragraph

Article in Sankhya. Series A. (2008), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

BARTSIMP: Flexible spatial covariate modeling and prediction using Bayesian Additive Regression Trees.Spatial and spatio-temporal epidemiology · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Sudipto BanerjeeUniversity of California Los Angeles, Los Angeles, USA.ORCID 0000-0002-2239-208X

Funding

Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health ApplicationsR01ES027027 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BANERJEE, SUDIPTO · 2017 to 2021

$1.7M

Flexible Bayesian Hierarchical Models for Estimating Inhalation ExposuresR01ES030210 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BANERJEE, SUDIPTO · 2019 to 2022

$1.6M

Bayesian Modeling and Inference for High-Dimensional Disease Mapping and Boundary Detection"R01GM148761 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sudipto Banerjee · 2023 to 2026

$1.2M

NIEHS NIH HHS R01 ES027027NIEHS NIH HHS R01 ES030210NIGMS NIH HHS R01 GM148761

6 · The paper itself

Abstract

This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the article proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatial finite populations are presented.

Indexed as

Bayesian inferencefinite population survey samplinggraphical modelshierarchical modelsPrimary 62F15Secondary 62D05spatial data

Identifiers

PMID40894087

DOI10.1007/s13171-024-00348-8

PMCPMC12396637

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.