A Unified Framework for Estimation of High-dimensional Conditional Factor Models

• URL: http://arxiv.org/abs/2209.00391v1
• Date: Thu, 1 Sep 2022 12:10:29 GMT
• Title: A Unified Framework for Estimation of High-dimensional Conditional Factor Models
• Authors: Qihui Chen
• Abstract summary: This paper develops a general framework for estimation of high-dimensional conditional factor models via nuclear norm regularization. We establish large sample properties of the estimators, and provide an efficient computing algorithm for finding the estimators. We apply the method to analyze the cross section of individual US stock returns, and find that imposing homogeneity may improve the model's out-of-sample predictability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper develops a general framework for estimation of high-dimensional conditional factor models via nuclear norm regularization. We establish large sample properties of the estimators, and provide an efficient computing algorithm for finding the estimators as well as a cross validation procedure for choosing the regularization parameter. The general framework allows us to estimate a variety of conditional factor models in a unified way and quickly deliver new asymptotic results. We apply the method to analyze the cross section of individual US stock returns, and find that imposing homogeneity may improve the model's out-of-sample predictability.

Related papers

• Approximate Message Passing for the Matrix Tensor Product Model [8.206394018475708]
  We propose and analyze an approximate message passing (AMP) algorithm for the matrix tensor product model. Building upon an convergence theorem for non-separable functions, we prove a state evolution for non-separable functions. We leverage this state evolution result to provide necessary and sufficient conditions for recovery of the signal of interest.
  arXiv  Detail & Related papers  (2023-06-27T16:03:56Z)
• Post-Selection Confidence Bounds for Prediction Performance [2.28438857884398]
  In machine learning, the selection of a promising model from a potentially large number of competing models and the assessment of its generalization performance are critical tasks. We propose an algorithm how to compute valid lower confidence bounds for multiple models that have been selected based on their prediction performances in the evaluation set.
  arXiv  Detail & Related papers  (2022-10-24T13:28:43Z)
• Distributional Gradient Boosting Machines [77.34726150561087]
  Our framework is based on XGBoost and LightGBM. We show that our framework achieves state-of-the-art forecast accuracy.
  arXiv  Detail & Related papers  (2022-04-02T06:32:19Z)
• Optimal regularizations for data generation with probabilistic graphical models [0.0]
  Empirically, well-chosen regularization schemes dramatically improve the quality of the inferred models. We consider the particular case of L 2 and L 1 regularizations in the Maximum A Posteriori (MAP) inference of generative pairwise graphical models.
  arXiv  Detail & Related papers  (2021-12-02T14:45:16Z)
• Optimizing model-agnostic Random Subspace ensembles [5.680512932725364]
  We present a model-agnostic ensemble approach for supervised learning. The proposed approach alternates between learning an ensemble of models using a parametric version of the Random Subspace approach. We show the good performance of the proposed approach, both in terms of prediction and feature ranking, on simulated and real-world datasets.
  arXiv  Detail & Related papers  (2021-09-07T13:58:23Z)
• Community Detection in the Stochastic Block Model by Mixed Integer Programming [3.8073142980733]
  Degree-Corrected Block Model (DCSBM) is a popular model to generate random graphs with community structure given an expected degree sequence. Standard approach of community detection based on the DCSBM is to search for the model parameters that are the most likely to have produced the observed network data through maximum likelihood estimation (MLE) We present mathematical programming formulations and exact solution methods that can provably find the model parameters and community assignments of maximum likelihood given an observed graph.
  arXiv  Detail & Related papers  (2021-01-26T22:04:40Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Slice Sampling for General Completely Random Measures [74.24975039689893]
  We present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables. The efficacy of the proposed algorithm is evaluated on several popular nonparametric models.
  arXiv  Detail & Related papers  (2020-06-24T17:53:53Z)
• Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation [74.06904875527556]
  We propose a generic and efficient segmentation framework to construct ensemble segmentation models. In the proposed method, ensemble models can be efficiently generated by using the layer selection method. We also devise a new pixel-wise uncertainty loss, which improves the predictive performance.
  arXiv  Detail & Related papers  (2020-05-21T16:08:38Z)
• Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants [94.46276668068327]
  In [1], an ensemble of randomly projected linear discriminants is used to classify datasets. We develop a consistent estimator of the misclassification probability as an alternative to the computationally-costly cross-validation estimator. We also demonstrate the use of our estimator for tuning the projection dimension on both real and synthetic data.
  arXiv  Detail & Related papers  (2020-04-17T12:47:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.