关注
Danica M. Ommen
Danica M. Ommen
在 iastate.edu 的电子邮件经过验证
标题
引用次数
引用次数
年份
Building a unified statistical framework for the forensic identification of source problems
DM Ommen, CP Saunders
Law, Probability and Risk 17 (2), 179-197, 2018
462018
An argument against presenting interval quantifications as a surrogate for the value of evidence
DM Ommen, CP Saunders, C Neumann
Science & Justice 56 (5), 383-387, 2016
302016
The characterization of Monte Carlo errors for the quantification of the value of forensic evidence
DM Ommen, CP Saunders, C Neumann
Journal of Statistical Computation and Simulation 87 (8), 1608-1643, 2017
262017
A problem in forensic science highlighting the differences between the Bayes factor and likelihood ratio
DM Ommen, CP Saunders
Statistical Science 36 (3), 344-359, 2021
192021
Score-based likelihood ratios to evaluate forensic pattern evidence
N Garton, D Ommen, J Niemi, A Carriquiry
arXiv preprint arXiv:2002.09470, 2020
122020
Approximate statistical solutions to the forensic identification of source problem
DM Ommen
South Dakota State University, 2017
102017
Handwriting identification using random forests and scorebased likelihood ratios
MQ Johnson, DM Ommen
Statistical Analysis and Data Mining: The ASA Data Science Journal 15 (3 …, 2022
92022
Advances toward validating examiner writership opinion based on handwriting kinematics
DM Ommen, C Fuglsby, MP Caligiuri
Forensic Science International 318, 110644, 2021
92021
Characterization and differentiation of aluminum powders used in improvised explosive devices–Part 1: Proof of concept of the utility of particle micromorphometry
JM Baldaino, DM Ommen, CP Saunders, J Hietpas, JA Buscaglia
Journal of forensic sciences 66 (1), 83-95, 2021
62021
Use of an Automated System to Evaluate Feature Dissimilarities in Handwriting Under a TwoStage Evaluative Process*,
C Fuglsby, C Saunders, DM Ommen, MP Caligiuri
Journal of Forensic Sciences 65 (6), 2080-2086, 2020
62020
Characterization and differentiation of aluminum powders used in improvised explosive devices. Part 2: Micromorphometric method refinement and preliminary statistical analysis
DM Ommen, JM Baldaino, CP Saunders, J Hietpas, JA Buscaglia
Journal of forensic sciences 67 (2), 505-515, 2022
52022
Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons
C Fuglsby, C Saunders, DM Ommen, JA Buscaglia, MP Caligiuri
Journal of Forensic Sciences 67 (2), 642-650, 2022
52022
Sourceanchored, traceanchored, and general match scorebased likelihood ratios for camera device identification
S Reinders, Y Guan, D Ommen, J Newman
Journal of Forensic Sciences 67 (3), 975-988, 2022
42022
Reconciling the Bayes Factor and Likelihood Ratio for Two Non-Nested Model Selection Problems
DM Ommen, CP Saunders
arXiv preprint arXiv:1901.09798, 2019
42019
Generalized fiducial factor: An alternative to the Bayes factor for forensic identification of source problems
JP Williams, DM Ommen, J Hannig
The Annals of Applied Statistics 17 (1), 378-402, 2023
32023
A Note on the specific source identification problem in forensic science in the presence of uncertainty about the background population
DM Ommen, CP Saunders, C Neumann
arXiv preprint arXiv:1503.08234, 2015
32015
Ensemble learning for score likelihood ratios under the common source problem
F Veneri, DM Ommen
Statistical Analysis and Data Mining: The ASA Data Science Journal 16 (6 …, 2023
22023
A statistical approach to aid examiners in the forensic analysis of handwriting
AM Crawford, DM Ommen, AL Carriquiry
Journal of Forensic Sciences 68 (5), 1768-1779, 2023
22023
A rotation-based feature and Bayesian hierarchical model for the forensic evaluation of handwriting evidence in a closed set
AM Crawford, DM Ommen, AL Carriquiry
The Annals of Applied Statistics 17 (2), 1127-1151, 2023
22023
An evaluation of score-based likelihood ratios for glass data
F Veneri, D Ommen
22021
系统目前无法执行此操作,请稍后再试。
文章 1–20