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Source: Scandinavian Journal of Statistics: Theory and Applications
Resulting in 2 citations.
1. Leung, Denis Heng Yan
Yamada, Ken
Zhang, Biao
Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression
Scandinavian Journal of Statistics 42,1 (March 2015): 155-179.
Also: https://onlinelibrary.wiley.com/doi/10.1111/sjos.12100
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Statistical Analysis; Wage Theory

Permission to reprint the abstract has not been received from the publisher.

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi-parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.
Bibliography Citation
Leung, Denis Heng Yan, Ken Yamada and Biao Zhang. "Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression." Scandinavian Journal of Statistics 42,1 (March 2015): 155-179.
2. Li, Rui
Leng, Chenlei
You, Jinhong
A Semiparametric Regression Model for Longitudinal Data with Non-stationary Errors
Scandinavian Journal of Statistics: Theory and Applications 44,4 (December 2017): 932-950.
Also: http://onlinelibrary.wiley.com/doi/10.1111/sjos.12284/full
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Modeling; Monte Carlo; Statistics

Permission to reprint the abstract has not been received from the publisher.

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean-covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non-linear, while the within-subject correlations are modelled by a non-stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non-parametric functions via B-spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non-parametric estimators. We further advocate a new model selection strategy in the varying-coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non-linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.
Bibliography Citation
Li, Rui, Chenlei Leng and Jinhong You. "A Semiparametric Regression Model for Longitudinal Data with Non-stationary Errors." Scandinavian Journal of Statistics: Theory and Applications 44,4 (December 2017): 932-950.