Inference in the presence of influential units and nonresponse for functional and non-functional survey data

对功能性和非功能性调查数据存在影响力单位和无响应的推断

基本信息

  • 批准号:
    RGPIN-2014-04905
  • 负责人:
  • 金额:
    $ 1.31万
  • 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:
    2016
  • 资助国家:
    加拿大
  • 起止时间:
    2016-01-01 至 2017-12-31
  • 项目状态:
    已结题

项目摘要

Nonresponse inevitably occurs in most, if not all, surveys. Essentially, survey statisticians distinguish unit nonresponse from item nonresponse. Unit nonresponse occurs when all the survey variables are missing or not enough usable information is available, whereas item nonresponse occurs when some but not all the survey variables have missing values. Weight adjustment procedures are generally used to treat unit nonresponse, whereas imputation is generally used to handle item nonresponse. The main objective when treating nonresponse is the reduction of the nonresponse bias, which occurs when respondents and nonrespondents are different with respect to the survey variables. In practice, surveys statisticians also face the problem of influential units. Influential units are correctly recorded and represent other population units similar in value. Their presence in the sample tends to make the classical estimators very unstable. Influential units occur when the distribution of the variables being collected is highly skewed or when some units have a large design weight. An estimator is said to be robust if it is not too sensitive to the presence of influential units. Robust estimators are biased but their mean square error is smaller than that of non-robust estimators. In some situations, the target parameter is not a mean real value but a mean function. For example, one may be interested in estimating the mean electricity consumption curve of a large number of consumers in a fixed time interval. We propose to study the problem of estimating the mean curve in the presence of missing data. We intend to establish the theoretical properties of estimators based on observed data and imputed data by means of nearest-neighbour imputation. Also, some units may be highly influential, which can make both the estimator of the mean curve and its variance very unstable. We plan to develop robust estimation procedures, study their theoretical properties and apply the proposed methods to real data. Robust small area estimation has received considerable attention in recent years. Most research has focussed on continuous characteristics of interest. Several robust versions of the empirical best linear unbiased predictor based on linear mixed models (LMM) have been proposed in the literature. In practice, many variables are categorical rather than continuous. As a result, methods based on LMMs are not suited. The objective is to propose a unified framework for robust small area estimation based on generalized LMMs so that robust predictors can be readily obtained for any type of variable. In practice, the target parameter may be a complex parameter; e.g., a quantile. Doubly robust procedures have been widely studied in the context of missing data. An estimation procedure is said to be doubly robust if it remains consistent if either the nonresponse model or the imputation model is correctly specified. So far, the literature has focussed on estimating a population mean. We plan to develop doubly protected estimation procedures for complex parameters and establish their theoretical properties. Finally, we propose to extend a recent concept called multiple robustness to finite population sampling. In practice, multiple nonresponse models and multiple imputation models may be fitted, each involving different subsets of covariates and possibly different link functions. An estimator is said to be multiply robust if it is consistent if any one of those multiple models, for either the propensity score or the characteristic of interest, is correctly specified. We also plan to develop multiply robust variance estimators that remain consistent for the true variance if any one of those multiple models is correct.
在大多数(如果不是全部的话)调查中不可避免地会出现无回应的情况。从本质上讲,调查统计学家区分了单位无反应和项目无反应。当所有的调查变量都缺失或可用信息不足时,就会出现单位无响应,而当一些但不是所有的调查变量都缺失值时,就会出现项目无响应。权重调整程序通常用于处理单位无反应,而归算程序通常用于处理项目无反应。处理无反应时的主要目标是减少无反应偏差,这种偏差发生在受访者和非受访者在调查变量方面不同的情况下。

项目成果

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Haziza, David其他文献

General purpose multiply robust data integration procedures for handling nonprobability samples
  • DOI:
    10.1111/sjos.12605
  • 发表时间:
    2022-08-12
  • 期刊:
  • 影响因子:
    1
  • 作者:
    Chen, Sixia;Haziza, David
  • 通讯作者:
    Haziza, David
MULTIPLY ROBUST NONPARAMETRIC MULTIPLE IMPUTATION FOR THE TREATMENT OF MISSING DATA
  • DOI:
    10.5705/ss.202017.0126
  • 发表时间:
    2019-10-01
  • 期刊:
  • 影响因子:
    1.4
  • 作者:
    Chen, Sixia;Haziza, David
  • 通讯作者:
    Haziza, David
Multiply robust imputation procedures for the treatment of item nonresponse in surveys
  • DOI:
    10.1093/biomet/asx007
  • 发表时间:
    2017-06-01
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Chen, Sixia;Haziza, David
  • 通讯作者:
    Haziza, David
A survey of bootstrap methods in finite population sampling
  • DOI:
    10.1214/16-ss113
  • 发表时间:
    2016-01-01
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Mashreghi, Zeinab;Haziza, David;Leger, Christian
  • 通讯作者:
    Leger, Christian
Model-Assisted Estimation Through Random Forests in Finite Population Sampling

Haziza, David的其他文献

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{{ truncateString('Haziza, David', 18)}}的其他基金

Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPIN-2019-05891
  • 财政年份:
    2022
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPIN-2019-05891
  • 财政年份:
    2021
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPAS-2019-00086
  • 财政年份:
    2020
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPIN-2019-05891
  • 财政年份:
    2020
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPIN-2019-05891
  • 财政年份:
    2019
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Robust inference for complex survey data
对复杂调查数据的稳健推断
  • 批准号:
    RGPAS-2019-00086
  • 财政年份:
    2019
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Inference in the presence of influential units and nonresponse for functional and non-functional survey data
对功能性和非功能性调查数据存在影响力单位和无响应的推断
  • 批准号:
    RGPIN-2014-04905
  • 财政年份:
    2018
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Inference in the presence of influential units and nonresponse for functional and non-functional survey data
对功能性和非功能性调查数据存在影响力单位和无响应的推断
  • 批准号:
    RGPIN-2014-04905
  • 财政年份:
    2017
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Inference in the presence of influential units and nonresponse for functional and non-functional survey data
对功能性和非功能性调查数据存在影响力单位和无响应的推断
  • 批准号:
    RGPIN-2014-04905
  • 财政年份:
    2015
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Inference in the presence of influential units and nonresponse for functional and non-functional survey data
对功能性和非功能性调查数据存在影响力单位和无响应的推断
  • 批准号:
    RGPIN-2014-04905
  • 财政年份:
    2014
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual

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