A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estim...

Asymptotically nonlocal field theories interpolate between Lee-Wick with multiple propagator poles, and ghost-free theories. Previous work on asymptotically scalar, Abelian, non-Abelian gauge has demonstrated the existence of an emergent regulator scale that is hierarchically smaller than lightest partner, in a limit where spectrum becomes dense decoupled. We generalize this construction to lin...

Seymour's Second Neighborhood Conjecture (SNC) states that every oriented graph contains a vertex whose second neighborhood is as large its first neighborhood. We investigate the SNC for orientations of both binomial and pseudo random graphs, verifying asymptotically almost surely (a.a.s.) all G(n,p) if limsupn→∞p<1/4; uniformly-random orientation each weakly (p,Anp)-bijumbled order n density p...

A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estim...

We investigate the problem of stable in-place merging from a ratio k = n m based point of view where m, n are the sizes of the input sequences with m ≤ n . We introduce a novel algorithm for this problem that is asymptotically optimal regarding the number of assignments as well as comparisons. Our algorithm uses knowledge about the ratio of the input sizes to gain optimality and does not stay i...

Most of the existing methods for missing data analysis are density based imputations. A serious drawback of these methods is that, when the observed data do not obey the assumed density, consequent inferences may not be reliable. In the context of mean and covariance structure analysis with missing data, use of a pseudo-maximum likelihood method has been proposed, but its properties have not be...