Damian Kozbur
Biographic Data
| ID | 8879312 |
|---|---|
| NAME | Damian Kozbur |
| GIVEN NAMES | Damian |
| FAMILY NAME | Kozbur |
| SIGNATURE | KOZBUR D |
| AFFILIATIONS | University of Zurich |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Inference for Dependent Data with Learned Clusters
This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the p…
Targeted Undersmoothing
This paper proposes a procedure for assessing the sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By…
Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables
This article studies nonparametric series estimation and inference for the effect of a single variable of interest x on an outcome y in the presence of potentially high-dimensional conditioning variables z. The context is an additively separable model E[y|x,z]=g0(x)+h0(z). The model is high-dimensional in the sense that the series of approximating functions for h0(z) can have more terms than the sample size, thereby allowing z potentially to have…
Inference in High-Dimensional Panel Models With an Application to Gun Control
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relativ…
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Inference in High-Dimensional Panel Models With an Application to Gun Control
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relativ…
Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables
This article studies nonparametric series estimation and inference for the effect of a single variable of interest x on an outcome y in the presence of potentially high-dimensional conditioning variables z. The context is an additively separable model E[y|x,z]=g0(x)+h0(z). The model is high-dimensional in the sense that the series of approximating functions for h0(z) can have more terms than the sample size, thereby allowing z potentially to have…
Targeted Undersmoothing
This paper proposes a procedure for assessing the sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By…
Inference for Dependent Data with Learned Clusters
This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the p…
Computer Science (4 works) · Artificial Intelligence (3 works) · Inference (3 works) · Mathematics (3 works) · Statistical Methods and Bayesian Inference (3 works) · Statistical Methods and Inference (3 works) · Statistics (3 works) · Applied Mathematics (2 works) · Econometrics (2 works) · Estimator (2 works)