Backfitting for large scale crossed random effects regressions

Laws of Large Numbers for Random Linear

The computational solution of large scale linear programming problems contains various difficulties. One of the difficulties is to ensure numerical stability. There is another difficulty of a different nature, namely the original data, contains errors as well. In this paper, we show that the effect of the random errors in the original data has a diminishing tendency for the optimal value as the...

Efficient moment calculations for variance components in large unbalanced crossed random effects models

Large crossed data sets, described by generalized linear mixed models, have become increasingly common and provide challenges for statistical analysis. At very large sizes it becomes desirable to have the computational costs of estimation, inference and prediction (both space and time) grow at most linearly with sample size. Both traditional maximum likelihood estimation and numerous Markov cha...

"Influence sketching": Finding influential samples in large-scale regressions

There is an especially strong need in modern largescale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack. In order to solve the “needle in the haystack" problem of which samples to inspect, we develop a new scalable version of Cook’s distance, a classical s...

Transformations of random regressions

Random Access Support for Large Scale VoD

The implementation of an interactive Video on Demand service is conventionally expensive because each viewer must be allocated a video stream. On the other hand, video streams can be multicast to a number viewers, reducing the system resources. This paper proposes a scheme, called Random Access PMC (RAPMC) which reduces the requirements for supporting an interactive VOD service. It is shown tha...