In this report, we analyze the asymptotic efficiency of self-adjusting contraction trees proposed as part of the Slider project [2, 3]. Self-adjusting contraction trees are used for incremental computation [1, 4, 5, 8]. Our analysis extends the asymptotic efficiency analysis of Incoop [6, 7]. We consider two different runs: the initial run of an Slider computation, where we perform a computatio...

A bstract Asymptotic symmetries in Carrollian gravitational theories 3+1 space and time dimensions obtained from “magnetic” “electric” ultrarelativistic contractions of General Relativity are analyzed. In both cases, parity conditions needed to guarantee a finite symplectic term, analogy with Einstein gravity. For the magnetic contraction, when Regge-Teitelboim imposed, asymptotic described by ...

Asymptotic couples (of H-type) try to capture the structure induced by the derivation of a Hardy field K on the value group of the natural valuation on K. In this note we continue the study of algebraic and model-theoretic aspects of asymptotic couples undertaken in [1]. We give a short exposition of some basic facts about asymptotic couples, and address a few topics left out in that paper: the...

Abstract Using the notion of strict h-contraction, existence results for Ψ-asymptotic equivalence two pairs (Lyapunov) matrix differential equations with integral term as right side and modified argument are given.

When a candidate model for data is nonidentifiable, conventional wisdom dictates that the model must be simplified somehow, in order to gain identifiability. We explore two scenarios involving mismeasured variables where in fact model expansion, as opposed to model contraction, might be used to obtain an identifiable model. We compare the merits of model contraction and model expansion. We also...

The aim of this article is to simplify Pfanzagl’s proof of consistency for asymptotic maximum likelihood estimators, and to extend it to more general asymptotic M -estimators. The method relies on the existence of a sort of contraction of the parameter space which admits the true parameter as a fixed point. The proofs are short and elementary.

The aim of this article is to simplify Pfanzagl’s proof of consistency for asymptotic maximum likelihood estimators, and to extend it to more general asymptotic M -estimators. The method relies on the existence of a sort of contraction of the parameter space which admits the true parameter as a fixed point. The proofs are short and elementary.