An imputation-based approach for parameter estimation in the presence of ambiguous censoring with application in industrial supply chain

This paper describes a novel approach based on "proportional imputation" when identical units produced in a batch have random but independent installation and failure times. The current problem is motivated by a real life industrial production-delivery supply chain where identical units are shipped after production to a third party warehouse and then sold at a future date for possible installation. Due to practical limitations, at any given time point, the exact installation as well as the failure times are known for only those units which have failed within that time frame after the installation. Hence, in-house reliability engineers are presented with a very limited, as well as partial, data to estimate different model parameters related to installation and failure distributions. In reality, other units in the batch are generally not utilized due to lack of proper statistical methodology, leading to gross misspecification. In this paper we have introduced a likelihood based parametric and computationally efficient solution to overcome this problem.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS348 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the Estimation of the incidence and Prevalence in Two-Phase Longitudinal Sampling Design

Two-phase sampling design is a common practice in many medical studies. Generally, the first-phase classification is fallible but relatively cheap, while the accurate second phase state-of-the-art medical diagnosis is complex and rather expensive to perform. When constructed efficiently it offers great potential for higher true case detection as well as for higher precision at a limited cost. In this article, we consider epidemiological studies with two-phase sampling design. However, instead of a single two-phase study, we consider a scenario where a series of two-phase studies are done in a longitudinal fashion on a cohort of interest. Another major design issue is non-curable pattern of certain disease (e.g. Dementia, Alzheimer\u27s etc.). Thus often the identified disease positive subjects are removed from the original population under observation, as they require clinical attention, which is quite different from the yet unidentified group. In this article, we motivated our methodology development from two real-life studies. We consider efficient and simultaneous estimation of prevalence as well incidence at multiple time points from a sampling design-based approach. We have explicitly shown the benefit of our developed methodology for an elderly population with significant burden of home-health care usage and at the high risk of major depressive disorder