Generative Learning of Heterogeneous Tail Dependence

• URL: http://arxiv.org/abs/2011.13132v2
• Date: Mon, 13 Nov 2023 01:52:24 GMT
• Title: Generative Learning of Heterogeneous Tail Dependence
• Authors: Xiangqian Sun, Xing Yan, Qi Wu
• Abstract summary: Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions. We devise a novel moment learning algorithm to learn the parameters. Results show that this framework gives better finite-sample performance compared to the copula-based benchmarks.
• Score: 13.60514494665717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also allowing heterogeneity and asymmetry in the tails of the marginals. A significant merit of our model structure is that it is not prone to error propagation in the parameter estimation process, hence very scalable, as the dimensions of datasets grow large. However, the likelihood methods are infeasible for parameter estimation in our case due to the lack of a closed-form density function. Instead, we devise a novel moment learning algorithm to learn the parameters. To demonstrate the effectiveness of the model and its estimator, we test them on simulated as well as real-world datasets. Results show that this framework gives better finite-sample performance compared to the copula-based benchmarks as well as recent similar models.

Related papers

• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Benign Overfitting in Time Series Linear Model with Over-Parameterization [5.68558935178946]
  We develop a theory for excess risk of the estimator under multiple dependence types. We show that the convergence rate of risks with short-memory processes is identical to that of cases with independent data.
  arXiv  Detail & Related papers  (2022-04-18T15:26:58Z)
• Nonparametric Functional Analysis of Generalized Linear Models Under Nonlinear Constraints [0.0]
  This article introduces a novel nonparametric methodology for Generalized Linear Models. It combines the strengths of the binary regression and latent variable formulations for categorical data. It extends recently published parametric versions of the methodology and generalizes it.
  arXiv  Detail & Related papers  (2021-10-11T04:49:59Z)
• T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs [0.0]
  In graph-structured data, structured sparsity and smoothness tend to cluster together. We propose a new prior for high dimensional parameters with graphical relations. We use it to detect structured sparsity and smoothness simultaneously.
  arXiv  Detail & Related papers  (2021-07-06T10:10:03Z)
• Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics [61.49826776409194]
  We analyze a corpus of models made publicly-available for a contest to predict the generalization accuracy of neural network (NN) models. We identify what amounts to a Simpson's paradox: where "scale" metrics perform well overall but perform poorly on sub partitions of the data. We present two novel shape metrics, one data-independent, and the other data-dependent, which can predict trends in the test accuracy of a series of NNs.
  arXiv  Detail & Related papers  (2021-06-01T19:19:49Z)
• 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)
• Amortized Bayesian model comparison with evidential deep learning [0.12314765641075436]
  We propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work.
  arXiv  Detail & Related papers  (2020-04-22T15:15:46Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Predicting Multidimensional Data via Tensor Learning [0.0]
  We develop a model that retains the intrinsic multidimensional structure of the dataset. To estimate the model parameters, an Alternating Least Squares algorithm is developed. The proposed model is able to outperform benchmark models present in the forecasting literature.
  arXiv  Detail & Related papers  (2020-02-11T11:57:07Z)

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.