Filter results by
Search HelpKeyword(s)
Subject
Author(s)
Results
All (5)
All (5) ((5 results))
- Articles and reports: 12-001-X202200200007Description:
Statistical inference with non-probability survey samples is a notoriously challenging problem in statistics. We introduce two new methods of nonparametric propensity score technique for weighting in the non-probability samples. One is the information projection approach and the other is the uniform calibration in the reproducing kernel Hilbert space.
Release date: 2022-12-15 - Articles and reports: 11-633-X2022007Description:
This paper investigates how Statistics Canada can increase trust by giving users the ability to authenticate data from its website through digital signatures and blockchain technology.
Release date: 2022-09-19 - Articles and reports: 12-001-X202200100007Description:
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in machine learning to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases.
Release date: 2022-06-21 - 4. Data ethics: An introduction ArchivedStats in brief: 89-20-00062022001Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics ethics must be upheld in order to ensure the appropriate use of data.
Release date: 2022-05-24 - Articles and reports: 12-001-X202100200003Description:
Calibration weighting is a statistically efficient way for handling unit nonresponse. Assuming the response (or output) model justifying the calibration-weight adjustment is correct, it is often possible to measure the variance of estimates in an asymptotically unbiased manner. One approach to variance estimation is to create jackknife replicate weights. Sometimes, however, the conventional method for computing jackknife replicate weights for calibrated analysis weights fails. In that case, an alternative method for computing jackknife replicate weights is usually available. That method is described here and then applied to a simple example.
Release date: 2022-01-06
Stats in brief (1)
Stats in brief (1) ((1 result))
- 1. Data ethics: An introduction ArchivedStats in brief: 89-20-00062022001Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics ethics must be upheld in order to ensure the appropriate use of data.
Release date: 2022-05-24
Articles and reports (4)
Articles and reports (4) ((4 results))
- Articles and reports: 12-001-X202200200007Description:
Statistical inference with non-probability survey samples is a notoriously challenging problem in statistics. We introduce two new methods of nonparametric propensity score technique for weighting in the non-probability samples. One is the information projection approach and the other is the uniform calibration in the reproducing kernel Hilbert space.
Release date: 2022-12-15 - Articles and reports: 11-633-X2022007Description:
This paper investigates how Statistics Canada can increase trust by giving users the ability to authenticate data from its website through digital signatures and blockchain technology.
Release date: 2022-09-19 - Articles and reports: 12-001-X202200100007Description:
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in machine learning to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases.
Release date: 2022-06-21 - Articles and reports: 12-001-X202100200003Description:
Calibration weighting is a statistically efficient way for handling unit nonresponse. Assuming the response (or output) model justifying the calibration-weight adjustment is correct, it is often possible to measure the variance of estimates in an asymptotically unbiased manner. One approach to variance estimation is to create jackknife replicate weights. Sometimes, however, the conventional method for computing jackknife replicate weights for calibrated analysis weights fails. In that case, an alternative method for computing jackknife replicate weights is usually available. That method is described here and then applied to a simple example.
Release date: 2022-01-06
Journals and periodicals (0)
Journals and periodicals (0) (0 results)
No content available at this time.
- Date modified: