The Experts below are selected from a list of 27741 Experts worldwide ranked by ideXlab platform

Kenneth Hill - One of the best experts on this subject based on the ideXlab platform.

  • Demographic techniques indirect estimation
    International Encyclopedia of the Social & Behavioral Sciences, 2001
    Co-Authors: Kenneth Hill
    Abstract:

    Indirect estimation methods have been developed to facilitate Measurement of Demographic processes in countries lacking accurate conventional data. The methods can be divided into two broad groups. ‘Indirect estimates’ seek to derive measures of Demographic parameters from indicators largely, but not entirely, determined by the parameter of interest. The methods adjust the indicator for the influence of other factors to obtain a ‘pure’ measure of the parameter required. ‘Consistency checks’ seek to compare measures of a given process derived from different sources, and, if the measures are found to be inconsistent, to use knowledge of typical error patterns as a basis for adjustment. Although indirect estimation is an interim solution to problems of data quality, it will remain an important element of Demographic Measurement in developing countries for the foreseeable future.

Maire Ni Bhrolchain - One of the best experts on this subject based on the ideXlab platform.

  • five reasons for measuring period fertility
    2008
    Co-Authors: Maire Ni Bhrolchain
    Abstract:

    Five reasons for measuring period fertility are distinguished: to describe fertility time trends, to explain these, to anticipate future population prospects, to provide input parameters for formal models, and to communicate with non-specialist audiences. The paper argues that not all measures are suitable for each purpose, and that tempo adjustment may be appropriate for some objectives but not others. In particular, it is argued that genuine timing effects do not bias or distort measures of period fertility as dependent variable. Several different concepts of bias or distortion are identified in relation to period fertility measures. Synthetic cohort indicators are a source of confusion since they conflate Measurement and forecasting. Anticipating future fertility is more akin to forecasting than to Measurement. Greater clarity about concepts and measures in the fertility arena could be achieved by a stronger emphasis on validation. Period incidence and occurrence-exposure rates have a straightforward interpretation. More complex period fertility measures are meaningful only if a direct or indirect criterion can be specified against which to evaluate them. Their performance against that criterion is what establishes them as valid or useful. Discussion of tempo adjustment and allied issues in Demographic Measurement might profit from the development of a theory of Measurement in demography, comparable to the axiomatic systems devised in e.g. physics, psychology and some areas of economics such as price index theory.

Rene Vidal - One of the best experts on this subject based on the ideXlab platform.

  • Improving age Measurement in low- and middle-income countries through computer vision: A test in Senegal
    'Max Planck Institute for Demographic Research', 2019
    Co-Authors: Stephane Helleringer, Chong You, Laurence Fleury, Laetitia Douillot, Insa Diouf, Cheikh Tidiane Ndiaye, Valerie Delaunay, Rene Vidal
    Abstract:

    Background: Age misreporting is pervasive in most low- and middle-income countries (LMIC). It may bias estimates of key Demographic indicators, such as those required to track progress towards sustainable development goals. Existing methods to improve age data are often ineffective, cannot be adopted on a large scale, and/or do not permit estimating age over the entire life course. Objective: We tested a computer vision approach, which produces an age estimate by analyzing a photograph of an individual's face. Methods: We constituted a small training dataset in a population of Senegal covered by a health and Demographic surveillance system (HDSS) since 1962. We collected facial images of 353 women aged 18 and above, whose age could be ascertained precisely using HDSS data. We developed automatic age estimation (AAE) systems through machine learning and cross-validation. Results: AAE was highly accurate in distinguishing women of reproductive age from women aged 50 and older (area under the curve > 0.95). It allowed estimating age in completed years, with a level of precision comparable to those obtained in European or East Asian populations with training datasets of similar sizes (mean absolute error = 4.62 years). Conclusions: Computer vision might help improve age ascertainment in Demographic datasets collected in LMICs. Further improving the accuracy of this approach will require constituting larger and more complete training datasets in additional LMIC populations. Contribution: Our work highlights the potential benefits of widely used computer science tools for improving Demographic Measurement in LMIC settings with deficient data

Stephane Helleringer - One of the best experts on this subject based on the ideXlab platform.

  • Improving age Measurement in low- and middle-income countries through computer vision: A test in Senegal
    'Max Planck Institute for Demographic Research', 2019
    Co-Authors: Stephane Helleringer, Chong You, Laurence Fleury, Laetitia Douillot, Insa Diouf, Cheikh Tidiane Ndiaye, Valerie Delaunay, Rene Vidal
    Abstract:

    Background: Age misreporting is pervasive in most low- and middle-income countries (LMIC). It may bias estimates of key Demographic indicators, such as those required to track progress towards sustainable development goals. Existing methods to improve age data are often ineffective, cannot be adopted on a large scale, and/or do not permit estimating age over the entire life course. Objective: We tested a computer vision approach, which produces an age estimate by analyzing a photograph of an individual's face. Methods: We constituted a small training dataset in a population of Senegal covered by a health and Demographic surveillance system (HDSS) since 1962. We collected facial images of 353 women aged 18 and above, whose age could be ascertained precisely using HDSS data. We developed automatic age estimation (AAE) systems through machine learning and cross-validation. Results: AAE was highly accurate in distinguishing women of reproductive age from women aged 50 and older (area under the curve > 0.95). It allowed estimating age in completed years, with a level of precision comparable to those obtained in European or East Asian populations with training datasets of similar sizes (mean absolute error = 4.62 years). Conclusions: Computer vision might help improve age ascertainment in Demographic datasets collected in LMICs. Further improving the accuracy of this approach will require constituting larger and more complete training datasets in additional LMIC populations. Contribution: Our work highlights the potential benefits of widely used computer science tools for improving Demographic Measurement in LMIC settings with deficient data

Chong You - One of the best experts on this subject based on the ideXlab platform.

  • Improving age Measurement in low- and middle-income countries through computer vision: A test in Senegal
    'Max Planck Institute for Demographic Research', 2019
    Co-Authors: Stephane Helleringer, Chong You, Laurence Fleury, Laetitia Douillot, Insa Diouf, Cheikh Tidiane Ndiaye, Valerie Delaunay, Rene Vidal
    Abstract:

    Background: Age misreporting is pervasive in most low- and middle-income countries (LMIC). It may bias estimates of key Demographic indicators, such as those required to track progress towards sustainable development goals. Existing methods to improve age data are often ineffective, cannot be adopted on a large scale, and/or do not permit estimating age over the entire life course. Objective: We tested a computer vision approach, which produces an age estimate by analyzing a photograph of an individual's face. Methods: We constituted a small training dataset in a population of Senegal covered by a health and Demographic surveillance system (HDSS) since 1962. We collected facial images of 353 women aged 18 and above, whose age could be ascertained precisely using HDSS data. We developed automatic age estimation (AAE) systems through machine learning and cross-validation. Results: AAE was highly accurate in distinguishing women of reproductive age from women aged 50 and older (area under the curve > 0.95). It allowed estimating age in completed years, with a level of precision comparable to those obtained in European or East Asian populations with training datasets of similar sizes (mean absolute error = 4.62 years). Conclusions: Computer vision might help improve age ascertainment in Demographic datasets collected in LMICs. Further improving the accuracy of this approach will require constituting larger and more complete training datasets in additional LMIC populations. Contribution: Our work highlights the potential benefits of widely used computer science tools for improving Demographic Measurement in LMIC settings with deficient data