Analysis

Skip to filters. View results.

What’s new on our website

View results

Filter results by

Search Help
Currently selected filters that can be removed
Remove Remove "Alavi, Asma" filter
Clear all

Keyword(s)

Subject

Year of publication

2 facets displayed. 0 facets selected.

Author(s)

3 facets displayed. 1 facets selected.
Sort Help
entries

Results

All (3)

All (3) ((3 results))

  • 1. The evolution of Canadian tourism, 1946 to 2015 Archived
    Articles and reports: 11-630-X2017001
    Description:

    This issue of Canadian Megatrends takes a historical look at Canadian tourism, describing the long-term changes in who has been visiting Canada—and where Canadians have been visiting.

    Release date: 2017-01-16
  • 2. Robust generalized regression estimation Archived
    Articles and reports: 12-001-X20040027752
    Description:

    The Best Linear Unbiased (BLU) estimator (or predictor) of a population total is based on the following two assumptions: i) the estimation model underlying the BLU estimator is correctly specified and ii) the sampling design is ignorable with respect to the estimation model. In this context, an estimator is robust if it stays close to the BLU estimator when both assumptions hold and if it keeps good properties when one or both assumptions are not fully satisfied. Robustness with respect to deviations from assumption (i) is called model robustness while robustness with respect to deviations from assumption (ii) is called design robustness. The Generalized Regression (GREG) estimator is often viewed as being robust since its property of being Asymptotically Design Unbiased (ADU) is not dependent on assumptions (i) and (ii). However, if both assumptions hold, the GREG estimator may be far less efficient than the BLU estimator and, in that sense, it is not robust. The relative inefficiency of the GREG estimator as compared to the BLU estimator is caused by widely dispersed design weights. To obtain a design-robust estimator, we thus propose a compromise between the GREG and the BLU estimators. This compromise also provides some protection against deviations from assumption (i). However, it does not offer any protection against outliers, which can be viewed as a consequence of a model misspecification. To deal with outliers, we use the weighted generalized M-estimation technique to reduce the influence of units with large weighted population residuals. We propose two practical ways of implementing M-estimators for multipurpose surveys; either the weights of influential units are modified and a calibration approach is used to obtain a single set of robust estimation weights or the values of influential units are modified. Some properties of the proposed approach are evaluated in a simulation study using a skewed finite population created from real survey data.

    Release date: 2005-02-03
  • 3. Evaluation and adjustment for non-response in the Canadian Labour Force Survey Archived
    Articles and reports: 11-522-X20030017598
    Description:

    This paper looks at descriptive statistics to evaluate non-response in the Labour Force Survey (LFS) and also at ways of improving the current methodology of making non-response adjustments.

    Release date: 2005-01-26
Stats in brief (0)

Stats in brief (0) (0 results)

No content available at this time.

Articles and reports (3)

Articles and reports (3) ((3 results))

  • 1. The evolution of Canadian tourism, 1946 to 2015 Archived
    Articles and reports: 11-630-X2017001
    Description:

    This issue of Canadian Megatrends takes a historical look at Canadian tourism, describing the long-term changes in who has been visiting Canada—and where Canadians have been visiting.

    Release date: 2017-01-16
  • 2. Robust generalized regression estimation Archived
    Articles and reports: 12-001-X20040027752
    Description:

    The Best Linear Unbiased (BLU) estimator (or predictor) of a population total is based on the following two assumptions: i) the estimation model underlying the BLU estimator is correctly specified and ii) the sampling design is ignorable with respect to the estimation model. In this context, an estimator is robust if it stays close to the BLU estimator when both assumptions hold and if it keeps good properties when one or both assumptions are not fully satisfied. Robustness with respect to deviations from assumption (i) is called model robustness while robustness with respect to deviations from assumption (ii) is called design robustness. The Generalized Regression (GREG) estimator is often viewed as being robust since its property of being Asymptotically Design Unbiased (ADU) is not dependent on assumptions (i) and (ii). However, if both assumptions hold, the GREG estimator may be far less efficient than the BLU estimator and, in that sense, it is not robust. The relative inefficiency of the GREG estimator as compared to the BLU estimator is caused by widely dispersed design weights. To obtain a design-robust estimator, we thus propose a compromise between the GREG and the BLU estimators. This compromise also provides some protection against deviations from assumption (i). However, it does not offer any protection against outliers, which can be viewed as a consequence of a model misspecification. To deal with outliers, we use the weighted generalized M-estimation technique to reduce the influence of units with large weighted population residuals. We propose two practical ways of implementing M-estimators for multipurpose surveys; either the weights of influential units are modified and a calibration approach is used to obtain a single set of robust estimation weights or the values of influential units are modified. Some properties of the proposed approach are evaluated in a simulation study using a skewed finite population created from real survey data.

    Release date: 2005-02-03
  • 3. Evaluation and adjustment for non-response in the Canadian Labour Force Survey Archived
    Articles and reports: 11-522-X20030017598
    Description:

    This paper looks at descriptive statistics to evaluate non-response in the Labour Force Survey (LFS) and also at ways of improving the current methodology of making non-response adjustments.

    Release date: 2005-01-26
Journals and periodicals (0)

Journals and periodicals (0) (0 results)

No content available at this time.

Report a problem or mistake on this page
Date modified:

Search help

How do I use the filters and the search box?

  • You can initiate a search by entering keywords or selecting filters (e.g., under Subject, Geography, etc.) on the left side of the page.

  • Filters can be used together or in any combination. Each time you select a filter, the results on the page are updated.

  • To start a new search, click on the Clear all button above the search box or deselect each filter.

  • Specified keywords and filters appear above the search box. You can deselect any or all the tags to refine or clear your search.

How do I refine my search?

  • You can enter keywords in the search box. It is not necessary to use “+” or “, “ or “AND”.

  • You can delete some or all of the keywords from your search string.

  • Quotes around keywords limit the search to a specific phrase.

    • For example, “labour force survey” will limit results to products containing this phrase.

  • Use “or” between keywords to obtain results that contain one or more search terms.

    • For example, labour or­ force or survey will limit results to products containing any or all of these words.

How does the search work?

  • This search looks for results that have your query term in the title, description, subject, geography, product number or other information about the product.

    • For example, when searching the term ‘Diseases’, all results with the word ‘Diseases’ in the title, description or tagged the subject appear.

  • The text within an article or publication are not searched. For full text searching of articles, use the site search.

Sort help

How are the results ordered?

The Sort by feature provides three options:

  • By date – sorts results by product release date starting with the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.

Sort help

How are the results ordered?

The Sort by feature provides four options:

  • By date – sorts results by product release date starting with the most recent.
  • By most popular – gives priority to results that are most frequently selected by users, followed by the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.