We consider maximum likelihood estimation of finite mixture of uniform distributions. We prove that the maximum likelihood estimator is strongly consistent, if the scale parameters of the component uniform distributions are restricted from below by exp(−nd), 0 < d < 1, where n is the sample size.

When distributed clients query or update shared data, eventual consistency can provide better availability than strong consistency models. However, programming and implementing such systems can be difficult unless we establish a reasonable consistency model, i.e. some minimal guarantees that programmers can understand and systems can provide effectively. To this end, we propose a novel consiste...

Our work deals with the synthesis of multispectral images at a better spatial resolution by the means of another image having such a resolution. We show the lack of standard procedure to assess the expected benefits of fused products. We discuss the case of the reference that is missing in most cases. We discuss the principles of the assessment protocol. One principle is that the constructed sy...

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.

We show a model construction for a system of higher-order illative combinatory logic Iω, thus establishing its strong consistency. We also use a variant of this construction to provide a complete embedding of first-order intuitionistic predicate logic with second-order propositional quantifiers into the system I0 of Barendregt, Bunder and Dekkers, which gives a partial answer to a question pose...

In this paper, regression function estimation from independent and identically distributed data is considered. We establish strong pointwise consistency of the famous Nadaraya-Watson estimator under weaker conditions which permit to apply kernels with unbounded support and even not integrable ones and provide a general approach for constructing strongly consistent kernel estimates of regression...

Causal consistency offers geo-distributed systems what ought to be a sweet option between the poor performance of strong consistency and the weak guarantees of eventual consistency. Yet, despite its appealing properties, causal consistency has seen limited adoption in industry, where systems have instead been clustering around the two extremes of eventual and strong consistency. We argue that t...

We consider the case 1 interval censorship model in which the survival time has an arbitrary distribution function F 0 and the inspection time has a discrete distribution function G. In such a model one is only able to observe the inspection time and whether the value of the survival time lies before or after the inspection time. We prove the strong consistency of the generalized maximum likeli...