Mark A. Wolters

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Wolters-Pubs

Mark Wolters

Publications

The publications in each category below are given in reverse chronological order.

Articles for which I am the sole or corresponding author are shaded in grey (for those that care about that sort of thing).

You can also see me on: Google Scholar, Microsoft Academic, ORCID, or ResearchGate.

Peer-Reviewed Articles

A Practical Implementation of Weighted Kernel Density Estimation for Handling Shape Constraints

Wolters, M. A. and Braun, W. J. (2018), STAT, 7: e202
The paper (open access)
This paper gives implementation details of the scdensity R package

Enforcing Shape Constraints on a Probability Density Estimate Using an Additive Adjustment Curve

Wolters, M. A. and Braun, W. J. (2018), Communications in Statistics—Simulation and Computation, 47(3), 672-691
Preprint with supplements     Or from the journal’s site
The method is implemented in R package scdensity

Better Autologistic Regression

Wolters, M. A. (2017). Frontiers in Applied Mathematics and Statistics, 3(24), 1-20.
The paper (open access)     See the related online demo

Large-Scale Transport of PM2.5 in the Lower Troposphere During Winter Cold Surges in China

Wang, J. et al. (2017), Scientific Reports, 7(13238), 1-10.
The paper (open access)

Big Data in Biosciences

Dean, C. B., Bull, S., Khurram, N., and Wolters, M. A. (2017), Wiley StatsRef: Statistics Reference Online.
Available here

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards

Wolters, M. A. and Dean, C. B. (2017), Statistics in Biosciences, 9(2), 622-645.
PDF file     Or from the journal’s site (open access)

A Genetic Algorithm for Selection of Fixed-Size Subsets, with Application to Design Problems

Wolters, M. A. (2015), Journal of Statistical Software, 68(1), 1-18.
The paper is here (open access)
The corresponding R package, kofnGA, is here

Issues in the Identification of Smoke in Hyperspectral Satellite Imagery—a Machine Learning Approach

Wolters, M. A. and Dean, C. B. (2015), Ch. 16 (pp. 349–373) in Current Air Quality Issues, InTech, Nejadkoorki, F. (ed.).
PDF file     Or from the publisher’s page (open access)
Sample R code accompanying the chapter

A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening

Wolters, M. A. (2012), Journal of Statistical Software, 46(6), 1–26.
Paper and MATLAB code here (open access)
A poster on this method
The method is implemented in R package scdensity

A Particle Swarm Algorithm with Broad Applicability in Shape-Constrained Estimation

Wolters, M. A. (2012), Computational Statistics and Data Analysis, 56(10), 2965–2975.
Paper and MATLAB code here

Simulated Annealing Model Search for Subset Selection in Screening Experiments

Wolters, M. A. and Bingham, D. (2011), Technometrics, 53, 225–237.
Paper and MATLAB code here

Other Publications

Methods for Shape-Constrained Kernel Density Estimation

Wolters, M. A. (2012), Ph.D. Thesis, University of Western Ontario. Link

A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening

Wolters, M. A. (2009), Technical Report TR-09-01, Department of Statistical and Actuarial Sciences, University of Western Ontario. PDF here

A Critical Look at Risk Measures: Why Statistics Don’t Always Mean What they Say

Wolters, M. A. (2008), Fraser Forum, Feb. 2008, 24-27.

Real Risks: Statistical Thinking and Risk Perception

Wolters, M. A. (2007), Fraser Institute Digital Publications, Dec. 2007. Link

Using Oversized Models to Find Active Variables in Screening Experiments

Wolters, M. A. (2007), M.Sc. Thesis, Simon Fraser Univeristy. Link