Publications by YoungStatS
Regularization by Noise for Stochastic Differential and Stochastic Partial Differential Equations
Regularization by Noise for Stochastic Differential and Stochastic Partial Differential Equations The regularizing effects of noisy perturbations of differential equations is a central subject of stochastic analysis. Recent breakthroughs initiated a new wave of interest, particularly concerning non-Markovian, infinite dimensional, and rough-stoc...
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Making an Online Risk Calculator
We first started making risk calculators with the website, http://nomograms.org. There are several here, and they are patient friendly. Later, we launched rcalc.ccf.org, which has many more risk calculators, although the intended audience is the clinician. It is considerably more expensive and time-consuming to make these patient friendly. There ...
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Online cash register data in the measurement of retail trade turnover
The Hungarian Central Statistical Office uses online cash register data for the measurement of retail trade and food services. The data source allows the statistical office to considerably cut administrative burden of data providers without quality loss in retail trade statistics. Online cash register data is widely used in tracking the latest de...
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Functional Regression Control Chart: a New Framework for Profile Monitoring
Introduction New statistical process control (SPC) methods have to be developed in order to handle more and more complex data, which are available because of the advent of new data acquisition technologies. In particular, in many practical situations the quality characteristic of a process can be modelled as a function defined on a compact domain...
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Causal discovery in the presence of discrete latent variables
We address the problem of causal structure learning in the presence of hidden variables. Given a target variable and a vector of covariates, we are trying to infer the set of observable causal parents of the target variable. There are many good reasons for being interested in causal predictors. Given a target variable $Y$, and a vector $X = (X^1,...
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The Mulitple Latent Block Model for mixed data
Abstract Co-clustering techniques, which group observations and features simultaneously, have proven to be efficient in summarising data sets. They exploit the dualism between rows and columns and the data set is summarized in blocks (the crossing of a row-cluster and a column-cluster). However, in the case of mixed data sets (with features of di...
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Machine learning for causal inference that works
I’ve kindly been invited to share a few words about a recent paper my colleagues and I published in Bayesian Analysis: “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”. In that paper, we motivate and describe a method that we call Bayesian causal forests (BCF), which is now imple...
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PLS for Big Data: A unified parallel algorithm for regularised group PLS
We look at the problem of learning latent structure between two blocks of data through the partial least squares (PLS) approach. These methods include approaches for supervised and unsupervised statistical learning. We review these methods and present approaches to decrease the computation time and scale the method to big data Given two blocks of...
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Locally adapative k-nearest neighbour classification
Abstract Binary classification is one of the cornerstones of modern data science, but, until recently, our understanding of classical methods such as the k-nn algorithm was limited to settings where feature vectors were compactly supported. Based on a new analysis of this classifier, we propose a variant with significantly lower risk for heavy-ta...
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Give me an adequate correlation: assessing relationships in percentage (or proportional) data
Correlations and negative bias We assume that you are quite familiar with the following problem. Consider a data set where the information is expressed in percentages or proportions. An example are household expenditures, given as average amounts (in Euros) the households are spending on food, housing, transportation, etc. Since the expenditures ...
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