Small area estimation, combining data from multiple sources, and inference from non-probability samples

小区域估计,结合多个来源的数据,以及非概率样本的推断

基本信息

  • 批准号:
    RGPIN-2019-06181
  • 负责人:
  • 金额:
    $ 2.19万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2021
  • 资助国家:
    加拿大
  • 起止时间:
    2021-01-01 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

Sample surveys, based on probability sampling, are widely used by Statistics Canada and other agencies to provide reliable current statistics on a variety of important topics. Efficient sampling designs are used to minimize the cost for a specified precision. Measures of variability (standard errors and confidence intervals) are used to assess the quality of the sample statistics. Surveys are typically designed to provide reliable estimates for sub-populations with sufficiently large samples. On the other hand, there is a growing demand for reliable local (or small area) statistics that are needed in formulating policies and programs, allocation of funds and marketing decisions. Sample sizes within local areas can be very small or even zero and the traditional direct estimates cannot provide local estimates with desired precision. It becomes necessary to resort to model-based methods that can borrow information across related domains through linking models based on auxiliary data such as censuses and administrative records. Statistics Canada has included model-based small area estimation in their modernization initiatives. I have done extensive research on model-based small area estimation that can lead to reliable estimates even when the sample sizes are small within small areas. My 2003 Wiley book [1] and its second edition [2] on small area estimation have become standard references to researchers and users. I propose to continue my work on small area estimation and provide robust methods to address practical issues such as model mis-specifications. Sample survey data are extensively used by social and health scientists and others to study relationships and testing hypotheses of interest, e.g. longitudinal data from the National Public Health Survey of Canada. Standard methods that ignore the design complexities can lead to erroneous inferences. To address this problem, Statistics Canada and some other agencies provide public-use data files containing adequate information for making valid inferences. I propose to develop unified methods that can provide valid tests of hypotheses and confidence intervals on parameters of interest from the public-use data files, using only standard software that incorporates survey weights supplied in the data files. Due to decreasing response rates and increasing costs associated with traditional sample surveys and availability of other sources of data, such as social media data, web surveys and administrative records, the topic of combining data from multiple sources to make inferences has received a lot of attention in recent years. Statistics Canada has given priority to this topic in their modernization of official statistics initiatives. I propose to conduct research on this important topic by developing suitable methods that can reduce selection bias associated with non-probability samples combined with probability samples and lead to efficient estimates.
加拿大统计局和其他机构广泛使用以概率抽样为基础的抽样调查,就各种重要专题提供可靠的最新统计数据。有效的抽样设计用于最小化指定精度的成本。变异性的量度(标准误差和置信区间)用于评估样本统计的质量。调查的目的通常是为样本足够大的次群体提供可靠的估计数。另一方面,对可靠的地方(或小地区)统计数据的需求日益增加,这是制定政策和方案、分配资金和营销决策所必需的。局部区域内的样本量可能很小甚至为零,传统的直接估计无法提供具有期望精度的局部估计。有必要采用基于模型的方法,通过将基于人口普查和行政记录等辅助数据的模型联系起来,借用相关领域的信息。加拿大统计局已将基于模型的小面积估算纳入其现代化举措。我对基于模型的小区域估计进行了广泛的研究,即使在小区域内样本量很小的情况下,也可以进行可靠的估计。我在2003年出版的Wiley著作[1]及其第二版[2]中关于小面积估算的内容已经成为研究人员和用户的标准参考。我建议继续我的工作,小面积估计,并提供强大的方法来解决实际问题,如模型误规格。抽样调查数据被社会和卫生科学家及其他人广泛用于研究关系和检验感兴趣的假设,例如加拿大国家公共卫生调查的纵向数据。忽略设计复杂性的标准方法可能导致错误的推断。为解决这一问题,加拿大统计局和其他一些机构提供了公共使用的数据文件,其中载有作出有效推断所需的充分信息。我建议开发统一的方法,可以提供有效的测试的假设和置信区间的参数感兴趣的公共使用的数据文件,只使用标准的软件,结合调查的权重提供的数据文件。由于传统抽样调查的答复率不断下降,成本不断增加,以及社交媒体数据、网络调查和行政记录等其他数据来源的可用性,近年来,将多个来源的数据结合起来进行推断的问题受到了广泛关注。加拿大统计局在官方统计现代化倡议中优先重视这一专题。我建议通过开发合适的方法,可以减少与非概率样本结合概率样本的选择偏差,并导致有效的估计,对这一重要课题进行研究。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Rao, Jonnagadda其他文献

Rao, Jonnagadda的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Rao, Jonnagadda', 18)}}的其他基金

Small area estimation, combining data from multiple sources, and inference from non-probability samples
小区域估计,结合多个来源的数据,以及非概率样本的推断
  • 批准号:
    RGPIN-2019-06181
  • 财政年份:
    2020
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Combining information from independent surveys, small area estimation of complex parameters, analysis of complex survey data
结合独立调查的信息,复杂参数的小区域估计,复杂调查数据的分析
  • 批准号:
    8856-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Combining information from independent surveys, small area estimation of complex parameters, analysis of complex survey data
结合独立调查的信息,复杂参数的小区域估计,复杂调查数据的分析
  • 批准号:
    8856-2013
  • 财政年份:
    2016
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Combining information from independent surveys, small area estimation of complex parameters, analysis of complex survey data
结合独立调查的信息,复杂参数的小区域估计,复杂调查数据的分析
  • 批准号:
    8856-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Combining information from independent surveys, small area estimation of complex parameters, analysis of complex survey data
结合独立调查的信息,复杂参数的小区域估计,复杂调查数据的分析
  • 批准号:
    8856-2013
  • 财政年份:
    2014
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Combining information from independent surveys, small area estimation of complex parameters, analysis of complex survey data
结合独立调查的信息,复杂参数的小区域估计,复杂调查数据的分析
  • 批准号:
    8856-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Inference from survey data and small area estimation
调查数据推论和小面积估计
  • 批准号:
    8856-2007
  • 财政年份:
    2012
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Inference from survey data and small area estimation
调查数据推论和小面积估计
  • 批准号:
    8856-2007
  • 财政年份:
    2010
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Inference from survey data and small area estimation
调查数据推论和小面积估计
  • 批准号:
    8856-2007
  • 财政年份:
    2009
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Inference from survey data and small area estimation
调查数据推论和小面积估计
  • 批准号:
    8856-2007
  • 财政年份:
    2008
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual

相似国自然基金

层出镰刀菌氮代谢调控因子AreA 介导伏马菌素 FB1 生物合成的作用机理
  • 批准号:
    2021JJ40433
  • 批准年份:
    2021
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
寄主诱导梢腐病菌AreA和CYP51基因沉默增强甘蔗抗病性机制解析
  • 批准号:
    32001603
  • 批准年份:
    2020
  • 资助金额:
    24.0 万元
  • 项目类别:
    青年科学基金项目
AREA国际经济模型的移植.改进和应用
  • 批准号:
    18870435
  • 批准年份:
    1988
  • 资助金额:
    2.0 万元
  • 项目类别:
    面上项目

相似海外基金

Clustered Coefficient Regression Model-Based Estimators in Small Area Estimation
小区域估计中基于聚类系数回归模型的估计器
  • 批准号:
    2316353
  • 财政年份:
    2023
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Standard Grant
Small Area Estimation for State and Local Health Departments
州和地方卫生部门的小面积估计
  • 批准号:
    10668454
  • 财政年份:
    2022
  • 资助金额:
    $ 2.19万
  • 项目类别:
Small Area Estimation for State and Local Health Departments
州和地方卫生部门的小面积估计
  • 批准号:
    10443373
  • 财政年份:
    2022
  • 资助金额:
    $ 2.19万
  • 项目类别:
Exploring spatio-temporal patterning of food insecurity within the island of Montreal: model-based small area estimation using the Canadian Community Health Surveys, 2011-2020.
探索蒙特利尔岛粮食不安全的时空格局:使用 2011-2020 年加拿大社区健康调查进行基于模型的小区域估计。
  • 批准号:
    462641
  • 财政年份:
    2022
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Operating Grants
Accessing the impact of youth suicide prevention program in small communities
了解小社区青少年自杀预防计划的影响
  • 批准号:
    10213274
  • 财政年份:
    2021
  • 资助金额:
    $ 2.19万
  • 项目类别:
Accessing the impact of youth suicide prevention program in small communities
了解小社区青少年自杀预防计划的影响
  • 批准号:
    10458497
  • 财政年份:
    2021
  • 资助金额:
    $ 2.19万
  • 项目类别:
Small Area Estimation for State and Local Health Departments
州和地方卫生部门的小面积估计
  • 批准号:
    10275680
  • 财政年份:
    2021
  • 资助金额:
    $ 2.19万
  • 项目类别:
Advancing complex models in small area estimation and spatial statistics
推进小区域估计和空间统计中的复杂模型
  • 批准号:
    RGPIN-2016-06046
  • 财政年份:
    2020
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Small area estimation, combining data from multiple sources, and inference from non-probability samples
小区域估计,结合多个来源的数据,以及非概率样本的推断
  • 批准号:
    RGPIN-2019-06181
  • 财政年份:
    2020
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
Advancing complex models in small area estimation and spatial statistics
推进小区域估计和空间统计中的复杂模型
  • 批准号:
    RGPIN-2016-06046
  • 财政年份:
    2019
  • 资助金额:
    $ 2.19万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了