Asymptotic Inference for Constrained Regression
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914233667551232 |
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| author | Sankaranarayanan, Madhav Hrytsenko, Yana Rotter, Jerome I. Sofer, Tamar Mukherjee, Rajarshi |
| author_facet | Sankaranarayanan, Madhav Hrytsenko, Yana Rotter, Jerome I. Sofer, Tamar Mukherjee, Rajarshi |
| contents | We consider statistical inference in high-dimensional regression problems under affine constraints on the parameter space. The theoretical study of this is motivated by the study of genetic determinants of diseases, such as diabetes, using external information from mediating protein expression levels. Specifically, we develop rigorous methods for estimating genetic effects on diabetes-related continuous outcomes when these associations are constrained based on external information about genetic determinants of proteins, and genetic relationships between proteins and the outcome of interest. In this regard, we discuss multiple candidate estimators and study their theoretical properties, sharp large sample optimality, and numerical qualities under a high-dimensional proportional asymptotic framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Asymptotic Inference for Constrained Regression Sankaranarayanan, Madhav Hrytsenko, Yana Rotter, Jerome I. Sofer, Tamar Mukherjee, Rajarshi Methodology Statistics Theory We consider statistical inference in high-dimensional regression problems under affine constraints on the parameter space. The theoretical study of this is motivated by the study of genetic determinants of diseases, such as diabetes, using external information from mediating protein expression levels. Specifically, we develop rigorous methods for estimating genetic effects on diabetes-related continuous outcomes when these associations are constrained based on external information about genetic determinants of proteins, and genetic relationships between proteins and the outcome of interest. In this regard, we discuss multiple candidate estimators and study their theoretical properties, sharp large sample optimality, and numerical qualities under a high-dimensional proportional asymptotic framework. |
| title | Asymptotic Inference for Constrained Regression |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2512.12953 |