R. W. Oldford (original) (raw)

Research overview

Statistical reasoning, exploratory data analysis, data visualization, and the development of interactive computational environments that support these activities, comprise the broad areas of my research interests.

Related to computational structure and methods for interactive data analysis, but somewhat removed from software implementation, I am also interested in the philosophical structure of statistical reasoning.

And I am always interested in applications of statistical methods in the natural and computational sciences.

Interests

Data Visualization
Data Analysis
Statistical application

Education

PhD in Statistics
University of Toronto
M.Sc. in Statistics
University of Toronto
B.Math. double major in Statistics and in Combinatorics & Optimization
University of Waterloo

Posters

with various students

Software Projects

Loon.ggplot

Turn ggplot2 graphic data structures into interactive loon plots

PairViz

Ordering visualizations using Graph Traversal

qqtest

Self Calibrating Quantile-Quantile Plots for Visual Testing

zenplots

Zigzag Expanded Navigation Plots

Loon

Exploratory interactive data visualization.

Featured Publications

May 2018 Significance, 15(3)

About "her emails"

Patterns in Secretary Clinton’s emails and a website (select “Code”) that allows anyone to interactively explore the patterns.

May 2018 SIAM Journal on Optimization, 28(1)

Euclidean distance matrix completion and point configurations from the minimal spanning tree

The paper introduces a special case of the Euclidean distance matrix completion problem of interest in statistical data analysis where only the minimal spanning tree distances are given and the matrix completion must preserve the minimal spanning tree. A guided random search algorithm is shown to outperform more standard optimization methods which also force peculiar and generally unwanted geometric structure on the point configurations their completions produce.

October 2017 Electronic Imaging 2018, Computational Imaging, XVI

Illuminant estimation using ensembles of multivariate regression trees

In this paper, we show that a simple and accurate ensemble model can be learned by (i) using multivariate regression trees to take into account that the chromaticity components of the illuminant are correlated and constrained, and (ii) fitting each tree by directly minimizing a loss function of interest—such as recovery angular error or reproduction angular error—rather than indirectly using the squared-error loss function as a surrogate. We show empirically that overall our method leads to improved performance on diverse image sets.

March 2016 The American Statistician, 70(1), pp. 74-90

Self-Calibrating Quantile–Quantile Plots

Quantile–quantile plots, or qqplots, are an important visual tool for many applications but their interpretation requires some care and often more experience. This apparent subjectivity is unnecessary. By drawing on the computational and display facilities now widely available, qqplots are easily enriched to help with their interpretation. An overview of quantile functions and quantile–quantile plots is presented against the backdrop of their early historical development. Strengths and shortcomings of the traditional display are described. A new enhanced qqplot, the self-calibrating qqplot, is introduced and demonstrated on a variety of examples—both synthetic and real. Real examples include normal qqplots, log-normal plots, half-normal plots for factorial experiments, qqplots for the average and standard deviation in process improvement applications, detection of multivariate outliers, and the comparison of empirical distributions. Self-calibration is had by visually incorporating sampling variation in the qqplot display in a variety of ways. The new qqplot is available through the function and R package qqtest.

December 2011 Computational Statistics, 26(4)

Graphs as navigational infrastructure for high dimensional data spaces

We propose using graph theoretic results to develop an infrastructure that tracks movement from a display of one set of variables to another. The illustrative example throughout is the real-time morphing of one scatterplot into another. Hurley and Oldford (J Comput Graph Stat 2010) made extensive use of the graph having variables as nodes and edges indicating a paired relationship between them. The present paper introduces several new graphs derivable from this one whose traversals can be described as particular movements through high dimensional spaces. These are connected to known results in graph theory and the graph theoretic results applied to the problem of visualizing high-dimensional data.

August 2000 Statistical Science, 15(3)

Scientific method, statistical method, and the speed of light

What is “statistical method”? Is it the same as “scientific method”? This paper answers the first question by specifying the elements and procedures common to all statistical investigations and organizing these into a single structure. This structure is illustrated by careful examination of the first scientific study on the speed of light carried out by A. A. Michelson in 1879. Our answer to the second question is negative. To understand this a history on the speed of light up to the time of Michelson’s study is presented. The larger history and the details of a single study allow us to place the method of statistics within the larger context of science.

R. W. Oldford (original) (raw)

Research overview

Interests

Education

Featured videos

Posters

Software Projects

Featured Publications

Recent & Upcoming Talks

Recent Publications

Popular Topics

Students

Current Grad Students

Ph.D. student in Statistics

Former PhD students

Data Scientist

Statistician

Research Advisor (Statistics)

Assistant Professor

Senior Researcher, Data Scientist

Former Masters students

Data Scientist

Senior Analyst

Middle Office and Valuation Senior Analyst

Ph.D. student in Computer Science

Game Mathematician

Research Advisor (Statistics)

Data Scientist

Professor

?????

Masters of Divinity student

Senior Advisor for Pricing and Contracting

Lead Software Engineer

(Deceased)

?????

Research Manager

?????

?????

Senior Fraud Analyst

Data Engineer

Data Scientist

Senior Researcher, Data Scientist

Marketing Campaign Design and Analytics Manager

Ph.D. student in Statistics