Papers by Andrey Skripnikov
Computational Statistics & Data Analysis, 2019
In a number of applications, one has access to high-dimensional time series data on several relat... more In a number of applications, one has access to high-dimensional time series data on several related subjects. A motivating application area comes from the neuroimaging field, such as brain fMRI time series data, obtained from various groups of subjects (cases/controls) with a specific neurological disorder. The problem of regularized joint estimation of multiple related Vector Autoregressive (VAR) models is discussed, leveraging a group lasso penalty in addition to a regular lasso one, so as to increase statistical efficiency of the estimates by borrowing strength across the models. A modeling framework is developed that it allows for both group-level and subject-specific effects for related subjects, using a group lasso penalty to estimate the former. An estimation procedure is introduced, whose performance is illustrated on synthetic data and compared to other state-of-the-art methods. Moreover, the proposed approach is employed for the analysis of resting state fMRI data. In particular, a group-level descriptive analysis is conducted for brain inter-regional temporal effects of Attention Deficit Hyperactive Disorder (ADHD) patients as opposed to controls, with the data available from the ADHD-200 Global Competition repository.
Journal of Applied Statistics, Jan 17, 2023
Given the importance of accurate team rankings in American college football (CFB)-due to heavy ti... more Given the importance of accurate team rankings in American college football (CFB)-due to heavy title and playoff implications-strides have been made to improve evaluation metrics across statistical categories, going from basic averages (e.g. points scored per game) to metrics that adjust for a team's strength of schedule, but one aspect that hasn't been emphasized is the complementary nature of American football. Despite the same team's offensive and defensive units typically consisting of separate player sets, some aspects of your team's defensive (offensive) performance may affect the complementary side: turnovers forced by your defense could lead to easier scoring chances for your offense, while your offense's ability to control the clock may help your defense. For 2009-2019 CFB seasons 1 , we incorporate natural splines with group penalty approaches to identify the most consistently influential features of complementary football in a data-driven way, conducting partially constrained optimization in order to additionally guarantee the full adjustment for strength of schedule and homefield factor. We touch on the issues arising due to reverse-causal nature of certain within-game dynamics, discussing several potential remedies. Lastly, game outcome prediction performances are compared across several ranking adjustment approaches for method validation purposes.
arXiv (Cornell University), Oct 22, 2022
Given the importance of accurate team rankings in American college football (CFB)-due to heavy ti... more Given the importance of accurate team rankings in American college football (CFB)-due to heavy title and playoff implications-strides have been made to improve evaluation metrics across statistical categories, going from basic averages (e.g. points scored per game) to metrics that adjust for a team's strength of schedule, but one aspect that hasn't been emphasized is the complementary nature of American football. Despite the same team's offensive and defensive units typically consisting of separate player sets, some aspects of your team's defensive (offensive) performance may affect the complementary side: turnovers forced by your defense could lead to easier scoring chances for your offense, while your offense's ability to control the clock may help your defense. For 2009-2019 CFB seasons 1 , we incorporate natural splines with group penalty approaches to identify the most consistently influential features of complementary football in a data-driven way, conducting partially constrained optimization in order to additionally guarantee the full adjustment for strength of schedule and homefield factor. We touch on the issues arising due to reverse-causal nature of certain within-game dynamics, discussing several potential remedies. Lastly, game outcome prediction performances are compared across several ranking adjustment approaches for method validation purposes.
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Papers by Andrey Skripnikov