VAR models [13] are a type of multi-equation model that linearly describe the simultaneous interactions and behaviour among a group of variables using only their own past. More specifically, a VAR is a model of simultaneous equations formed by a system of equations in which the contemporary values of model variables do not appear in any explanatory variable in the equations. The set of explanat...

The Predictive Least Squares (PLS) model selection criterion is known to be consistent in the context of linear regression. For small sample sizes, however, it can exhibit erratic behavior. We show that this shortcoming can be amended by incorporating a Student’s t-distribution into PLS. The resulting criterion is shown to be asymptotically equivalent to PLS but significantly more robust for sm...

By using a fair comparison method we show that contrary to the general belief the conventional LMS, when in training mode, does not necessarily outperform the popular blind LMS (BLMS). With the help of a constrained MMSE criterion we identify the correct trained version which is guaranteed to have uniformly superior performance over BLMS since it maximizes the SIR over an algorithmic class cont...

Identifying effects of actions (treatments) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference. This identification is made difficult by the presence of confounders which can be related to both treatment and outcome variables. Confounders are often handled, both in theory and in practice, by adjusting for covariates, in other words c...

The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with multicollinearity, Ridge, Liu, Ridge-type biased estimators have been presented statistical literature. recently proposed Kibria-Lukman estimator one estimators. literature has compared others using mean square error criterion for linear model. It was...

Pairwise comparison is an important tool in multi-attribute decision making. Pairwise comparison matrices (PCM) have been applied for ranking criteria and for scoring alternatives according to a given criterion. Our paper presents a special application of incomplete PCMs: ranking of professional tennis players based on their results against each other. The selected 25 players have been on the t...

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some...

This paper considers the design of linear-phase finite impulse response digital filters using an L1 optimality criterion. The motivation for using such filters as well as a mathematical framework for their design is introduced. It is shown that L1 filters possess flat passbands and stopbands while keeping the transition band comparable to that of least-squares filters. The uniqueness of L1-base...