【摘 要】
：
In this paper we investigate methods for adjustment for covariate measurement errors in complex surveys. Focus is on the adjustment for covariate measuremen
【机 构】
：
UniversityofHelsinki,FinlandandSocialInsuranceInstitution,Finland
【出 处】
：
The 24th International Workshop on Matrices and Statistics(第
论文部分内容阅读
In this paper we investigate methods for adjustment for covariate measurement errors in complex surveys. Focus is on the adjustment for covariate measurement errors in logistic regression for cluster correlated data. Complexity in this situation arises from correlation of observations due to cluster sampling. The adjustment methods which will be studied are Multiple Imputation and Regression Calibration. Some information about the measurement error must be available. This information is provided from validation study data. An interesting approach dealing measurement errors is multipleimputation for measurement errors (Cole et al., 2006; Padilla et al., 2009). In this approach measurement errors are treated as a missing data problem. Regression Calibration method is widely applied and studied (Rosner et al., 1989; Kuha, 1994; Spiegelman et al., 2000, 2001; Messer and Natarajan, 2008; Skrondal and Kuha, 2012). Regression calibration is a statistical method for adjusting point and interval estimates for bias due to measurement error.
其他文献
This presentation considers the application of Moore-Penrose generalized inverses in the expressing matrix spaces including the span of the products of the
In this talk we have a focus on the prediction problem within the framework of the general possibly singular Gauss-Markov model. Based on results of the pow
In this paper, a generalized difference-based estimator is introduced for the vector parameter $eta$ in partially linear model when the errors are correlate
Privacy concerns on data drive an applied field of statistics: Statistical Disclosure Control or Limitation (SDC or SDL). This field requires modeling of a
We will present several recent results on the structure of adjacency and coherency preservers on hermitian matrices. Applications in mathematical physics wi
First, we show analytically that the nexus between Least Squares (LS) estimators of multiple and simple regression coefficients are exactly the same as betw
This paper presents a family of models for a stationary non-negative first order integer-valued random variables based on the Pegram and thinning operators.
In this paper, under the assumption of normality, we give the comparison of unbiased estimator under the Pitmans measure of closeness (PMC). Its application
In this paper, we study the localization of matrix spectra inside a given sector of the complex plane, including matrix stability and eigenvalue positivity.
Methods of survival analysis (e.g., the Cox proportional hazards model) require that the event time be measured with respect to some origin time. The choice