11 citations · 13 across the 8 of their papers we have counts for
8 papers
Resolving Jeffreys-Lindley Paradox
Priyantha Wijayatunga
Jeffreys-Lindley paradox is a case where frequentist and Bayesian hypothesis testing methodologies contradict with each other. This has caused confusion among data analysts for sel…
On Associative Confounder Bias
Priyantha Wijayatunga
Conditioning on some set of confounders that causally affect both treatment and outcome variables can be sufficient for eliminating bias introduced by all such confounders when est…
Viewing Simpson's Paradox
Priyantha Wijayatunga
Well known Simpson's paradox is puzzling and surprising for many, especially for the empirical researchers and users of statistics. However there is no surprise as far as mathemati…
A geometric view on Pearson's correlation coefficient and a generalization of it to non-linear dependencies
Priyantha Wijayatunga
Measuring strength or degree of statistical dependence between two random variables is a common problem in many domains. Pearson's correlation coefficient is an accurate measur…
Resolving the Lord's Paradox
Priyantha Wijayatunga
An explanation to Lord's paradox using ordinary least square regression models is given. It is not a paradox at all, if the regression parameters are interpreted as predictive or a…
Probabilistic Analysis of Balancing Scores for Causal Inference
Priyantha Wijayatunga
Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of causal effect of…
Causal Effect Estimation Methods
Priyantha Wijayatunga
Relationship between two popular modeling frameworks of causal inference from observational data, namely, causal graphical model and potential outcome causal model is discussed. Ho…
A Consistency Result for Bayes Classifiers with Censored Response Data
Priyantha Wijayatunga, Xavier de Luna
Naive Bayes classifiers have proven to be useful in many prediction problems with complete training data. Here we consider the situation where a naive Bayes classifier is trained w…