Missing at random: a stochastic process perspective

Distinguishing "Missing at Random"i and "Missing Completely at Randomn"

What is the difference between missing completely at random and missing at random?

The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable advantages. The purpose of this article is to clarify how 'missing at random' differs from 'missing completely at random' via an imagined dialogue be...

On Testing the Missing at Random Assumption

Most approaches to learning from incomplete data are based on the assumption that unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption. In this paper we investigate a method for testing the mar assumption in the presence of other distributional...

Missing at Random in Graphical Models

The notion of missing at random (MAR) plays a central role in the theory underlying current methods for handling missing data. However the standard definition of MAR is difficult to interpret in practice. In this paper, we assume the missing data model is represented as a directed acyclic graph that not only encodes the dependencies among the variables but also explicitly portrays the causal me...

a stochastic perspective to random ship heave motion based on different noises

the stochastic models for ship heave motion in irregular sea waves based on white and colored noises are examined. for this purpose the deterministic model transfers to stochastic models by adding different noise terms in force and then these models are solved analytically and numerically. finally an investigation is undertaken to examine the parameter estimation problem of second order stochas...