Artem Prokhorov
Biographic Data
| ID | 8921189 |
|---|---|
| NAME | Artem Prokhorov |
| GIVEN NAMES | Artem |
| FAMILY NAME | Prokhorov |
| SIGNATURE | PROKHOROV A |
| AFFILIATIONS | The University of Sydney |
| ORCID | 0000-0003-4046-7994 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Change-Point Detection in Time Series Using Mixed Integer Programming
We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the l0-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding prova…
Running a double-blind true social experiment with a goal oriented adaptive AI-based conversational agent in educational research
This study introduces an innovative AI-facilitated interview-like survey system generating a combination of qualitative and quantitative data insights for higher education research. We employed a goal oriented adaptive AI-based Conversational Agent (AICA) which collected data directly from 1223 participants globally and ran a double-blind true social experiment online. During interviews, the AI established strong rapport with the participants, of…
Ds-Heck
We extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity scenario. The standard lasso estimation is known to under-select causing an omitted variable bias in addition to the sample selection bias. We outline the required adjustments needed to restore consistency of lasso-based estimation and inference for vector-valued parameters of interest in such models. The a…
Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors
A Simple Estimator of Two‐Dimensional Copulas, with Applications1
Copulas are distributions with uniform marginals. Non‐parametric copula estimates may violate the uniformity condition in finite samples. We look at whether it is possible to obtain valid piecewise linear copula densities by triangulation. The copula property imposes strict constraints on design points, making an equi‐spaced grid a natural starting point. However, the mixed‐integer nature of the problem makes a pure triangulation approach impract…
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A Simple Estimator of Two‐Dimensional Copulas, with Applications1
Copulas are distributions with uniform marginals. Non‐parametric copula estimates may violate the uniformity condition in finite samples. We look at whether it is possible to obtain valid piecewise linear copula densities by triangulation. The copula property imposes strict constraints on design points, making an equi‐spaced grid a natural starting point. However, the mixed‐integer nature of the problem makes a pure triangulation approach impract…
Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors
Ds-Heck
We extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity scenario. The standard lasso estimation is known to under-select causing an omitted variable bias in addition to the sample selection bias. We outline the required adjustments needed to restore consistency of lasso-based estimation and inference for vector-valued parameters of interest in such models. The a…
Running a double-blind true social experiment with a goal oriented adaptive AI-based conversational agent in educational research
This study introduces an innovative AI-facilitated interview-like survey system generating a combination of qualitative and quantitative data insights for higher education research. We employed a goal oriented adaptive AI-based Conversational Agent (AICA) which collected data directly from 1223 participants globally and ran a double-blind true social experiment online. During interviews, the AI established strong rapport with the participants, of…
Change-Point Detection in Time Series Using Mixed Integer Programming
We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the l0-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding prova…
Computer Science (3 works) · Econometrics (3 works) · Estimator (3 works) · Mathematics (3 works) · Monetary Policy and Economic Impact (3 works) · Statistics (3 works) · Applied Mathematics (2 works) · Mathematical optimization (2 works) · Statistical Methods and Inference (2 works) · AI in Service Interactions (1 work)