ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478227

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478228

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

Statistical agencies use a variety of disclosure control policies with ad hoc justification in disseminating data. The issues involved are clarified here by showing that several of these policies are special cases of a general disclosure-limiting (DL) approach based on predictive distributions and uncertainty functions. A user's information posture regarding a target is represented by one predictive distribution before data release and another predictive distribution after data release. A user's lack of knowledge about the target at any time is measured by an uncertainty function applied either to the current predictive distribution or to the current predictive distribution and the previously held predictive distribution. Common disclosure control policies, such as requiring released cell relative frequencies to be bounded away from both zero and one, are shown to be equivalent to disclosure rules that allow data release only if specific uncertainty functions at particular predictive distributions exceed a limit. Data transformations, such as aggregation and cell suppression, that are intended to reduce the extent of disclosure are analyzed in simple but realistic scenarios.

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478229

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478230

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478231

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478232

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478233

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478234

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

The U.S. Bureau of the Census imputes missing income items in the income supplement of the Current Population Survey (CPS) by a technique commonly known as the CPS hot deck. This article compares CPS hot deck imputations of wages and salary amounts with alternatives based on regression models for the logarithm of wages and salary and for the wage rate. Comparisons are effected by comparing imputations with an Internal Revenue Service (IRS) wages and salary amount found by an exact match of CPS data to IRS records. Although limitations in the matching and in the comparison variable preclude a definitive conclusion, we find that (a) the CPS hot deck does not underestimate income aggregates to any serious extent; (b) model-based alternatives have slightly smaller mean absolute error than the hot deck, when comparable data bases of respondents are used to carry out imputations; and (c) multivariate models for imputing recipiency, weeks and hours worked, and earnings need to be developed to provide realistic competitors to the current hot deck method.

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478235

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

This article considers the problem of using categorical data from a panel survey in which there is nonrandom nonresponse to estimate gross flows. The methods are illustrated for the case of estimating gross flows in labor force participation using data from the Canadian Labour Force Survey. Three models are proposed that allow nonresponse to be related to employment classification, time, or both employment classification and time. Maximum likelihood estimation is used to fit the models to a single panel of Labour Force Survey data.

ISSN:0162-1459    

DOI:10.1080/01621459.1986.10478236

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor