Dean A. Bodenham
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Dean A. Bodenham has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Niall M. Adams
Department of Mathematics Imperial College London South Kensington Campus SW7 2AZ London U.K
4 shared publications - Yijin Zeng
Department of Mathematics Imperial College London South Kensington Campus SW7 2AZ London U.K
4 shared publications Niall M. Adams
Department of Mathematics, Imperial College London, London, UK
2 shared publications- Andrej Srakar
Institute for Economic Research and University of Ljubljana , Ljubljana
1 shared publication - Ayse Ulgen
Affiliations Department of Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom, Department of Biostatistics, Faculty of Medicine, Girne American University, Karmi, Cyprus
1 shared publication - Elizabeth Eisenhauer
Elizabeth W. Eisenhauer holds a PhD in statistics from Penn State and is a senior statistical associate at Westat, where her work spans study design, programme evaluation, sampling, and weighting
1 shared publication
Latest Publications
- 13 August 2026
- Published by Oxford University Press (OUP) in Significance
- Vol. 23 (5) , 18-21
The thrill of the new
Exciting modern statistical methods can bring about huge leaps forward in science. Elizabeth Eisenhauer, Maria Nareklishvili, Ayse Ulgen, Andrej Srakar and Dean Bodenham, members of the RSS Emerging Applications Section committee, describe some recent applications of three of these methods: natural experiments, citizen science and changepoint detection
- 13 August 2026
- Published by Oxford University Press (OUP) in Significance
- Vol. 23 (5) , 18-21
The thrill of the new
Exciting modern statistical methods can bring about huge leaps forward in science. Elizabeth Eisenhauer, Maria Nareklishvili, Ayse Ulgen, Andrej Srakar and Dean Bodenham, members of the RSS Emerging Applications Section committee, describe some recent applications of three of these methods: natural experiments, citizen science and changepoint detection
- Open Access
Scale two-sample testing with arbitrarily missing data
This work proposes a novel rank-based scale two-sample testing method for univariate, distinct data when a subset of the data may be missing. Our approach is based on mathematically tight bounds of the Ansari-Bradley test statistic in the presence of missing data, and rejects the null hypothesis if the test statistic is significant regardless of the missing values. This proposed scale testing method is then combined with the location testing method proposed by Zeng et al. (2024) using the Holm-Bonferroni correction for location-scale testing. We show that our methods control the Type I error regardless of the values of the missing data. Simulation results demonstrate that our methods have good statistical power, typically when less than 10% of the data are missing, while other missing data methods, such as case deletion or imputation methods, fail to control the Type I error when the data are missing not at random. We illustrate the proposed location-scale testing method on hepatitis C virus dataset where a subset of values is unobserved.
Open AccessScale two-sample testing with arbitrarily missing data
This work proposes a novel rank-based scale two-sample testing method for univariate, distinct data when a subset of the data may be missing. Our approach is based on mathematically tight bounds of the Ansari-Bradley test statistic in the presence of missing data, and rejects the null hypothesis if the test statistic is significant regardless of the missing values. This proposed scale testing method is then combined with the location testing method proposed by Zeng et al. (2024) using the Holm-Bonferroni correction for location-scale testing. We show that our methods control the Type I error regardless of the values of the missing data. Simulation results demonstrate that our methods have good statistical power, typically when less than 10% of the data are missing, while other missing data methods, such as case deletion or imputation methods, fail to control the Type I error when the data are missing not at random. We illustrate the proposed location-scale testing method on hepatitis C virus dataset where a subset of values is unobserved.
- Open Access
Exact Bounds of Spearman's footrule in the Presence of Missing Data with Applications to Independence Testing
This work studies exact bounds of Spearman's footrule between two partially observed $n$-dimensional distinct real-valued vectors $X$ and $Y$. The lower bound is obtained by sequentially constructing imputations of the partially observed vectors, each with a non-increasing value of Spearman's footrule. The upper bound is found by first considering the set of all possible values of Spearman's footrule for imputations of $X$ and $Y$, and then the size of this set is gradually reduced using several constraints. Algorithms with computational complexities $O(n^2)$ and $O(n^3)$ are provided for computing the lower and upper bound of Spearman's footrule for $X$ and $Y$, respectively. As an application of the bounds, we propose a novel two-sample independence testing method for data with missing values. Improving on all existing approaches, our method controls the Type I error under arbitrary missingness. Simulation results demonstrate our method has good power, typically when the proportion of pairs containing missing data is below $15\%$.Open AccessExact Bounds of Spearman's footrule in the Presence of Missing Data with Applications to Independence Testing
This work studies exact bounds of Spearman's footrule between two partially observed $n$-dimensional distinct real-valued vectors $X$ and $Y$. The lower bound is obtained by sequentially constructing imputations of the partially observed vectors, each with a non-increasing value of Spearman's footrule. The upper bound is found by first considering the set of all possible values of Spearman's footrule for imputations of $X$ and $Y$, and then the size of this set is gradually reduced using several constraints. Algorithms with computational complexities $O(n^2)$ and $O(n^3)$ are provided for computing the lower and upper bound of Spearman's footrule for $X$ and $Y$, respectively. As an application of the bounds, we propose a novel two-sample independence testing method for data with missing values. Improving on all existing approaches, our method controls the Type I error under arbitrary missingness. Simulation results demonstrate our method has good power, typically when the proportion of pairs containing missing data is below $15\%$. - Open Access
- 1 January 2025
- Published by Institute of Mathematical Statistics in Electronic Journal of Statistics
- Vol. 19 (2) , 6103-6140
Exact bounds of Spearman’s footrule in the presence of missing data with applications to independence testing
This work studies exact bounds of Spearman’s footrule in the presence of missing data for two n-dimensional distinct real-valued vectors X and Y. The lower bound is obtained by sequentially constructing imputations of the partially observed vectors, each with a non-increasing value of Spearman’s footrule. The upper bound is found by first considering the set of all possible values of Spearman’s footrule for imputations of X and Y, and then the size of this set is gradually reduced using several constraints. Algorithms with computational complexities O(n2) and O(n3) are provided for computing these tight lower and upper bounds, respectively. As an application, we propose a novel two-sample independence testing method for data with missing values. Improving on all existing approaches, our method controls the Type I error under arbitrary missingness. Simulation results demonstrate that our method has good power, typically when the proportion of pairs containing missing data is below 15%. We illustrate our method on real-world data by comparing the dependence of yearly precipitation levels in the United Kingdom, Germany and the Unites States.
- 1 January 2025
- Published by Institute of Mathematical Statistics in Electronic Journal of Statistics
- Vol. 19 (2) , 6103-6140
Open AccessExact bounds of Spearman’s footrule in the presence of missing data with applications to independence testing
This work studies exact bounds of Spearman’s footrule in the presence of missing data for two n-dimensional distinct real-valued vectors X and Y. The lower bound is obtained by sequentially constructing imputations of the partially observed vectors, each with a non-increasing value of Spearman’s footrule. The upper bound is found by first considering the set of all possible values of Spearman’s footrule for imputations of X and Y, and then the size of this set is gradually reduced using several constraints. Algorithms with computational complexities O(n2) and O(n3) are provided for computing these tight lower and upper bounds, respectively. As an application, we propose a novel two-sample independence testing method for data with missing values. Improving on all existing approaches, our method controls the Type I error under arbitrary missingness. Simulation results demonstrate that our method has good power, typically when the proportion of pairs containing missing data is below 15%. We illustrate our method on real-world data by comparing the dependence of yearly precipitation levels in the United Kingdom, Germany and the Unites States.
- Open Access
On two-sample testing for data with arbitrarily missing values
We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is a theoretical extension of the Wilcoxon-Mann-Whitney test that controls the Type I error by providing exact bounds for the test statistic after accounting for the number of missing values. Greater statistical power is shown when the method is extended to account for a bounded domain. Furthermore, exact bounds are provided on the proportions of data that can be missing in the two samples while yielding a significant result. Simulations demonstrate that our method has good power, typically for cases of $10\%$ to $20\%$ missing data, while standard imputation approaches fail to control the Type I error. We illustrate our method on complex clinical trial data in which patients' withdrawal from the trial lead to missing values.Open AccessOn two-sample testing for data with arbitrarily missing values
We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is a theoretical extension of the Wilcoxon-Mann-Whitney test that controls the Type I error by providing exact bounds for the test statistic after accounting for the number of missing values. Greater statistical power is shown when the method is extended to account for a bounded domain. Furthermore, exact bounds are provided on the proportions of data that can be missing in the two samples while yielding a significant result. Simulations demonstrate that our method has good power, typically for cases of $10\%$ to $20\%$ missing data, while standard imputation approaches fail to control the Type I error. We illustrate our method on complex clinical trial data in which patients' withdrawal from the trial lead to missing values. - Open Access
- 22 December 2023
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 86 (2) , 315
Dean Bodenham and Niall Adams’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’
Dean Bodenham, Niall Adams; Dean Bodenham and Niall Adams's contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects o
- 22 December 2023
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 86 (2) , 315
Open AccessDean Bodenham and Niall Adams’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’
Dean Bodenham, Niall Adams; Dean Bodenham and Niall Adams's contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects o
- Open Access
- 27 July 2023
- Published by Springer Nature in Statistics and Computing
- Vol. 33 (5) , 1-14
euMMD: efficiently computing the MMD two-sample test statistic for univariate data
The maximum mean discrepancy (MMD) test is a nonparametric kernelised two-sample test that, when using a characteristic kernel, can detect any distributional change between two samples. However, when the total number of $$d$$ -dimensional observations is $$n$$ , direct computation of the test statistic is $$\mathcal {O}(dn^2 )$$ . While approximations with lower computational complexity are known, more efficient methods for computing the exact test statistic are unknown. This paper provides an exact method for computing the MMD test statistic for the univariate case in $$\mathcal {O}(n\log n)$$ using the Laplacian kernel. Furthermore, this exact method is extended to an approximate method for $$d$$ -dimensional real-valued data also with complexity log-linear in the number of observations. Experiments show that this approximate method can have good statistical performance when compared to the exact test, particularly in cases where $$d> n$$ .- 27 July 2023
- Published by Springer Nature in Statistics and Computing
- Vol. 33 (5) , 1-14
Open AccesseuMMD: efficiently computing the MMD two-sample test statistic for univariate data
The maximum mean discrepancy (MMD) test is a nonparametric kernelised two-sample test that, when using a characteristic kernel, can detect any distributional change between two samples. However, when the total number of $$d$$ -dimensional observations is $$n$$ , direct computation of the test statistic is $$\mathcal {O}(dn^2 )$$ . While approximations with lower computational complexity are known, more efficient methods for computing the exact test statistic are unknown. This paper provides an exact method for computing the MMD test statistic for the univariate case in $$\mathcal {O}(n\log n)$$ using the Laplacian kernel. Furthermore, this exact method is extended to an approximate method for $$d$$ -dimensional real-valued data also with complexity log-linear in the number of observations. Experiments show that this approximate method can have good statistical performance when compared to the exact test, particularly in cases where $$d> n$$ . - Open Access
- 18 July 2016
- Published by Springer Nature in Statistics and Computing
- Vol. 27 (5) , 1257-1270
Continuous monitoring for changepoints in data streams using adaptive estimation
- 18 July 2016
- Published by Springer Nature in Statistics and Computing
- Vol. 27 (5) , 1257-1270
Open AccessContinuous monitoring for changepoints in data streams using adaptive estimation
- Open Access
- 14 June 2015
- Published by Springer Nature in Statistics and Computing
- Vol. 26 (4) , 917-928
A comparison of efficient approximations for a weighted sum of chi-squared random variables
- 14 June 2015
- Published by Springer Nature in Statistics and Computing
- Vol. 26 (4) , 917-928
Open AccessA comparison of efficient approximations for a weighted sum of chi-squared random variables