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Source: Journal of the American Statistical Association
Resulting in 5 citations.
| 1. |
Borus, Michael E. Nestel, Gilbert |
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Response Bias in Reports of Father's Education and Socioeconomic Status Journal of the American Statistical Association 68,344 (December 1973): 816-820. Also: http://www.jstor.org/stable/2284505 Cohort(s): Older Men, Young Men Publisher: American Statistical Association Keyword(s): Educational Attainment; Family Influences; Fathers, Influence; Intergenerational Patterns/Transmission; Socioeconomic Status (SES) This article compares independent interview responses of fathers and their sons to questions about the educational attainment and occupational status of the father. There is a high degree of congruence between the son's and father's estimates of both measures. When, however, reporting differences are regressed on various demographic characteristics reported by the son, the responses of youths with certain characteristics are found to deviate significantly from their fathers. |
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Bibliography Citation
Borus, Michael E. and Gilbert Nestel. "Response Bias in Reports of Father's Education and Socioeconomic Status." Journal of the American Statistical Association 68,344 (December 1973): 816-820.
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| 2. |
Hoshino, Takahiro |
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Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference Journal of the American Statistical Association 108,504 (2013): 1189-1204. Also: http://www.tandfonline.com/doi/full/10.1080/01621459.2013.835656#.UtVUBxBwjpU Cohort(s): NLSY79 Publisher: American Statistical Association Keyword(s): Bayesian; Modeling; Wages We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on a potential outcomes. The model uses the probit stick-breaking process mixture (PSBPM) proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture (DPM) modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no parametric model assumption for the assignment model and conditional distribution of the covariate vector. The proposed estimation method is more robust than maximum likelihood estimation, in that it does not require knowledge of the full joint distribution of potential outcomes, covariates, and assignments. In addition, the method is more efficient than fully nonparametric Bayes methods. We apply this model to infer the differential effects of cognitive and noncognitive skills on the wages of production and non-production workers using panel data from the National Longitudinal Survey of Youth in 1979. The study also presents the causal effect of online word-of-mouth on website browsing behavior. |
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Bibliography Citation
Hoshino, Takahiro. "Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference." Journal of the American Statistical Association 108,504 (2013): 1189-1204.
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| 3. |
Manski, Charles F. Sandefur, Gary D. McLanahan, Sara S. Powers, Daniel A. |
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Alternative Estimates of the Effects of Family Structure During Childhood on High School Graduation Journal of the American Statistical Association 87,417 (March 1992): 25-37. Also: http://www.jstor.org/stable/2290448 Cohort(s): NLSY79 Publisher: American Statistical Association Keyword(s): Educational Attainment; Family Structure; Heterogeneity; High School Completion/Graduates; Parental Influences; Racial Differences A good deal of research in the past few years has found significant relationships between family structure during childhood and various outcomes during the teen and early adult years. There may, however, be unmeasured variables which affect both family structure and teen or early adult outcomes. The apparent effects of family structure may be due to these unmeasured variables, which affect both the likelihood of maintaining an intact marriage and parenting effectiveness. The authors estimate a model that attempts to take this unmeasured heterogeneity into account. Another weakness of past studies is that they make very strong assumptions about the relationship between family structure and early outcomes. Relaxing these assumptions, estimate nonparametric bounds on the magnitude of the relationship between family structure and early outcomes are estimated. |
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Bibliography Citation
Manski, Charles F., Gary D. Sandefur, Sara S. McLanahan and Daniel A. Powers. "Alternative Estimates of the Effects of Family Structure During Childhood on High School Graduation." Journal of the American Statistical Association 87,417 (March 1992): 25-37.
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| 4. |
Quintana, Fernando A. Newton, Michael A. |
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Assessing the Order of Dependence for Partially Exchangeable Binary Data Journal of the American Statistical Association 93,441 (March 1998): 194-202. Also: http://www.jstor.org/stable/2669616 Cohort(s): NLSY79 Publisher: American Statistical Association Keyword(s): Data Analysis; Data Quality/Consistency; Employment, Youth; Health/Health Status/SF-12 Scale; Markov chain / Markov model; Monte Carlo; Statistical Analysis The problem we consider is how to assess the order of serial dependence within partially exchangeable binary sequences. We obtain exact conditional tests comparing any two orders by finding the conditional distribution of data given certain transition counts. These tests are facilitated with a new Monte Carlo scheme. Asymptotic tests are also discussed. In particular, we show that the likelihood ratio tests have an asymptotic chi-square distribution, thus generalizing the results of Billingsley (1961) for the particular case of Markov chains. We apply these methods to several data sets, and perform a simulation to study their properties. This article is concerned with the nonparametric statistical analysis of multiple binary sequences, a commonly occurring data structure. One example that we consider comes from dairy science, where each of a number of cows is tested for the presence of a pathogen infection throughout the lactation cycle. Sports statistics provide our second example, in which each of many baseball players produces a sequence of hits/no hits over the course of a season (Albright 1993). A third example comes from the National Longitudinal Survey of Youth (NLSY), in which the employment and health status of a group of young people are monitored yearly by questionnaire (see Borus 1984). It is natural to allow correlation within each binary sequence when formulating models for such data. Among the various approaches, a particularly simple model says that each binary sequence is the realization of a Markov chain having some order of serial dependence. Zeroth-order chains correspond to independence, first-order chains exhibit serial dependence on the most recent binary variable, second-order chains depend on the most recent pair of variables, and so on. |
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Bibliography Citation
Quintana, Fernando A. and Michael A. Newton. "Assessing the Order of Dependence for Partially Exchangeable Binary Data." Journal of the American Statistical Association 93,441 (March 1998): 194-202.
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| 5. |
Tan, Zhiqiang |
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Marginal and Nested Structural Models Using Instrumental Variables Journal of the American Statistical Association 105,489 (March 2010): 157-169. Also: http://pubs.amstat.org/doi/abs/10.1198/jasa.2009.tm08299 Cohort(s): Young Men Publisher: American Statistical Association Keyword(s): Educational Returns; Modeling; Propensity Scores; Variables, Instrumental The objective of many scientific studies is to evaluate the effect of a treatment on an outcome of interest ceteris paribus. Instrumental variables (IVs) serve as an experimental handle, independent of potential outcomes and potential treatment status and affecting potential outcomes only through potential treatment status. We propose marginal and nested structural models using IVs, in the spirit of marginal and nested structural models under no unmeasured confounding. A marginal structural IV model parameterizes the expectations of two potential outcomes under an active treatment and the null treatment respectively, for those in a covariate-specific subpopulation who would take the active treatment if the instrument were externally set to each specific level. A nested structural IV model parameterizes the difference between the two expectations after transformed by a link function and hence the average treatment effect on the treated at each instrument level. We develop IV outcome regression, IV propensity score weighting, and doubly robust methods for estimation, in parallel to those for structural models under no unmeasured confounding. The regression method requires correctly specified models for the treatment propensity score and the outcome regression function. The weighting method requires a correctly specified model for the instrument propensity score. The doubly robust estimators depend on the two sets of models and remain consistent if either set of models are correctly specified. We apply our methods to study returns to education using data from the National Longitudinal Survey of Young Men. [ABSTRACT FROM AUTHOR] Copyright of Journal of the American Statistical Association is the property of American Statistical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
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Bibliography Citation
Tan, Zhiqiang. "Marginal and Nested Structural Models Using Instrumental Variables." Journal of the American Statistical Association 105,489 (March 2010): 157-169.
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