Erard, Brian (2017): Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach.
Preview |
PDF
MPRA_paper_79927.pdf Download (2MB) | Preview |
Abstract
Often providers of a program or a service have detailed information about their clients, but only very limited information about potential clients. Likewise, ecologists frequently have extensive knowledge regarding habitats where a given animal or plant species is known to be present, but they lack comparable information on habitats where they are certain not to be present. In epidemiology, comprehensive information is routinely collected about patients who have been diagnosed with a given disease; however, commensurate information may not be available for individuals who are known to be free of the disease. While it may be highly beneficial to learn about the determinants of participation (in a program or service) or presence (in a habitat or of a disease), the lack of a comparable sample of observations on subjects that are not participants (or that are non-present) precludes the application of standard qualitative response models, such as logit or probit. In this paper, we present some new qualitative response estimators that can be applied by combining information from a primary sample of participants with a general sample from the overall population. Our new estimators rival the best existing estimators for use control sampling. Furthermore, these new estimators can be applied to stratified samples even when the stratification criteria are unknown. The estimators are also readily generalized to accommodate polychotomous response problems.
Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach. Available from: https://www.researchgate.net/publication/317731280_Modeling_Qualitative_Outcomes_by_Supplementing_Participant_Data_with_General_Population_Data_A_Calibrated_Qualitative_Response_Estimation_Approach [accessed Jun 28, 2017].
Item Type: | MPRA Paper |
---|---|
Original Title: | Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach |
English Title: | Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach |
Language: | English |
Keywords: | Qualitative response, Probit, Logit, Case Control Sampling, Use Control Sampling, Presence Pseudo-Absence Sampling, Contaminated Controls, Supplementary Sampling, Prevalence, Take-Up, Habitat Selection |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation |
Item ID: | 79927 |
Depositing User: | Brian Erard |
Date Deposited: | 29 Jun 2017 15:22 |
Last Modified: | 30 Sep 2019 13:01 |
References: | Breslow, N.E. 1996. Statistics in Epidemiology: The Case-Control Study, Journal of the American Statistical Association, 91(433), 14-28. Burden, B.C., D.T. Canon, K.R. Mayer, and D.P. Moynihan. 2014. Election Laws, Mobilization, and Turnout: The Unanticipated Consequences of Election Reform, American Journal of Political Science, 58(1), 95-109. Erard, B., J. Guyton, P. Langetieg, M. Payne, and A. Plumley. 2016. What Drives Income Tax Filing Compliance? IRS Research Bulletin, Publication 1500, Washington, DC: Internal Revenue Service, 32-37. Cosslett, S.R. 1981. Efficient Estimation of Discrete Choice Models, in Structural Analysis of Discrete Data with Econometric Applications, ed. C. Manski and D. McFadden, Cambridge: MIT Press, 51-111. Imbens, G.W. 1996. An Efficient Method of Moments Estimator for Discrete Choice Models with Choice –Based Sampling. Econometrica, 60(5), 1187-1214. Keating, K.A. and S. Cherry 2004. Use and Interpretation of Logistic Regression in Habitat Selection Studies. Journal of Wildlife Management, 68(4), 774-789. Lancaster, T. and G. Imbens 1996. Case Controlled Studies with Contaminated Controls. Journal of Econometrics, 71, 145-160. Lele, S.R. 2009. A New Method for Estimation of Resource Selection Probability Function. Journal of Wildlife Management, 73(1), 122-127. Lele, S.R. and J.L. Keim 2006. Weighted Distributions and Estimation of Resource Selection Probability Functions. Ecology, 87(12), 3021-3028. Manski, C.F. and D. McFadden 1981. Alternative Estimators and Sample Designs for Discrete Choice Analysis, in Structural Analysis of Discrete Data with Econometric Applications, ed. C. Manski and D. McFadden, Cambridge: MIT Press, 2-49. Phillips, S.J. and J. Elith 2013. On Estimating Probability of Presence from Use-Availability or Presence-Background Data. Ecology, 94(6), 1409-1419. Phillips, S.J. and J. Elith. 2011. Logistic Methods for Resource Selection Functions and Presence-only Species Distribution Models, in Proceedings of the 25th AAAI Conference on Artificial Intelligence, San Francisco, California, 1384-1389. Rosenman, R., S. Goates, and L. Hill 2012. Participation in Universal Prevention Programs. Applied Economics, 44(2), 219-28. Steinberg, D. and N.S. Cardell 1992. Estimating Logistic Regression Models When the Dependent Variable Has No Variance. Communication in Statistics –Theory and Methods, 21(2), 423-450. Ward, G., T. Hastie, S. Barry, J. Elith, and J.R. Leathwick 2009. Presence-Only Data and the EM Algorithm. Biometrics, 65, 554-563. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/79927 |
Available Versions of this Item
- Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach. (deposited 29 Jun 2017 15:22) [Currently Displayed]