In much of applied statistics variables of interest are measured with error. In particular, regression with covariates that are subject to measurement error requires adjustment to avoid biased estimates and invalid inference. We consider two aspects of this problem. Detection Limits (DL) arise...

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts? awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved...

Abstract Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects,...

Monitoring soil quality provides indispensable inputs for effective policy advice, but very few poorer countries can implement high-quality surveys on soil. We offer an alternative, low-cost imputation-based approach to generating various soil quality indicators. The estimation results validate...

Smallholder farming dominates agriculture in poorer countries. Yet, traditional recall-based surveys on smallholder farming in these countries face challenges with seasonal variations, high survey costs, poor record-keeping, and technical capacity constraints resulting in significant recall...

Smallholder farming dominates agriculture in poorer countries. Yet, traditional recall-based surveys on smallholder farming in these countries face challenges with seasonal variations, high survey costs, poor record-keeping, and technical capacity constraints resulting in significant recall...