Justin Petrovich

• PhD Statistics
• Professor (Assistant) at Saint Vincent College

In this work we propose a functional concurrent regression model to estimate labor supply elasticities over the years 1988 through 2014 using Current Population Survey data. Assuming, as is common , that individuals' wages are endogenous, we introduce instrumental variables in a two-stage least squares approach to estimate the desired labor supply...

This study aims to explore the effect of University Counseling Center (UCC) treatment in a nationally representative sample of 101,354 college students with suicide risk variables (i.e. recent suicidal ideation (SI), recent SI and history of suicide attempts (SA), history of SA) as compared to those without suicide risk seeking services from 160 UC...

This work presents a new approach, called Multiple Imputation of Sparsely‐sampled Functions at Irregular Times (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 observed points per curve grows...

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 engaged in suicide behavior during treatment. Regression analyses revealed that suicide behavior was po...

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 observed points per curve grows sufficiently quickly with the sample size. In contrast, MISFIT is base...

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 semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression functi...

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 their asymptotic normality.