We investigate estimation in Poisson regression model when the count response is right-censored and censoring indicators are missing at random. propose several estimators based on calibration, multiple imputation augmented inverse probability weighting methods. Under appropriate regularity conditions, we prove consistency of our derive their asymptotic distributions. Simulation experiments carr...

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the lack of support to quickly identify sub-optimal conf...

As space-object catalogs move from being maintained by general perturbations to special perturbations, and as the number of detectable objects in space increase, sequential (recurrent) estimators have some significant advantages over batch processing that should not be overlooked. A primary advantage of a sequential estimator is that the time evolution of the satellite state error covariance ca...

the empirical bayes estimators of treatment effects in a factorial experiment were derived and their asymptotic properties were explored. it was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. a bootstrap analysis was performed to ill...

Sensor Calibration is the process of determining the intrinsic (e.g., focal length) and extrinsic (i.e., position and orientation (pose) with respect to the world, or to another sensor) parameters of a sensor. This task is an essential prerequisite for many applications in robotics, computer vision, and augmented reality. For example, in the field of robotics, in order to fuse measurements from...

Traditional methods for sampling weight adjustment involve weighting class adjustment for nonresponse bias reduction, followed by post-stratification (raking-ratio or regression) adjustment for coverage bias reduction, and then trimming (or winsorization) of extreme weights for variance reduction followed by final post-stratification to meet desired control totals and for further variance reduc...

In this paper, we propose a novel extrinsic calibration method for camera networks using a sphere as the calibration object. First of all, we propose an easy and accurate method to estimate the 3D positions of the sphere center w.r.t. the local camera coordinate system. Then, we propose to use orthogonal procrustes analysis to pairwise estimate the initial camera relative extrinsic parameters b...

Consider the field of rational functions Kpxq for a field K and let d dx be the usual derivative. Then Kpxq is a differential field. We call Kpxqtyu the ring of differential polynomials. Its elements are polynomials in y and the derivatives of y, i. e. Kpxqtyu Kpxqry, y1, y2, . . .s. An algebraic ordinary differential equation (AODE) is one of the form F px, y, y1, . . . , ypnqq 0 where F P Kpx...