Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Justin Petrovich"'
Publikováno v:
Cogent Mental Health. 2
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69a39d0cf5c287a546f5d5226bfdded9
https://doi.org/10.1080/28324765.2023.2169583
https://doi.org/10.1080/28324765.2023.2169583
Autor:
Justin Petrovich, Matthew Reimherr
Publikováno v:
Statistics & Probability Letters. 130:42-48
In FPCA methods, it is common to assume that the eigenvalues are distinct in order to facilitate theoretical proofs. We relax this assumption, provide a stochastic expansion for the estimated functional principal component projections, and establish
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::718748a7dae6b12625c7987d9fa823a3
https://doi.org/10.1016/j.spl.2017.07.004
https://doi.org/10.1016/j.spl.2017.07.004
Autor:
Jeffrey A. Hayes, Louis G. Castonguay, Rebecca A. Janis, Benjamin D. Locke, Justin Petrovich, Ying Yang
Publikováno v:
Journal of counseling psychology. 67(1)
This study sought to identify predictors of suicidal behavior among college students who are psychotherapy clients, as well as to determine underlying classes of clients with suicidal ideation. Data were gathered from 101,570 clients, 391 of whom eng
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26bad7c2e217e63cdeec5535877486d5
https://pubmed.ncbi.nlm.nih.gov/31556625
https://pubmed.ncbi.nlm.nih.gov/31556625
Autor:
Mariya P. Shiyko, Donna L. Coffman, John J. Dziak, Runze Li, Matthew Reimherr, Saul Shiffman, Justin Petrovich
Publikováno v:
Statist. Surv. 13 (2019), 150-180
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::043101eabad2b74b8d4b6374aca24f7e
https://projecteuclid.org/euclid.ssu/1573009381
https://projecteuclid.org/euclid.ssu/1573009381
This work presents a new approach, called MISFIT, for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not allow for consistent estimation unless one assumes that the number of obs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10763e165b0e6f12ac12d4b6bcfc096c
http://arxiv.org/abs/1805.08518
http://arxiv.org/abs/1805.08518