Copertina

Cluster-based Regression using Variational Inference and Applications in Financial Forecasting

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nagpal, Udai, Nagpal, Krishan
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Statistical Finance Machine Learning Methodology 68T09 (Primary), 62P20 (Secondary) G.3; I.2; I.5
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
di: Jean, N., et al.
Pubblicazione: (2025)

The Choice of Normalization Influences Shrinkage in Regularized Regression
di: Larsson, Johan, et al.
Pubblicazione: (2025)

Kernel-based independence and mean independence tests for weakly dependent data
di: Diz-Castro, Daniel, et al.
Pubblicazione: (2026)

Physics-Informed Singular-Value Learning for Cross-Covariances Forecasting in Financial Markets
di: Manolakis, Efstratios, et al.
Pubblicazione: (2026)

Spurious Predictability in Financial Machine Learning
di: Nikolopoulos, Sotirios D.
Pubblicazione: (2026)

Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations
di: Maturo, Fabrizio, et al.
Pubblicazione: (2024)

A Structural Text-Based Scaling Model for Analyzing Political Discourse
di: Vávra, Jan, et al.
Pubblicazione: (2024)

Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice
di: Jerzak, Connor T., et al.
Pubblicazione: (2024)

FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework
di: Boyeena, Nitin Sagar, et al.
Pubblicazione: (2024)

DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learning
di: Tirumala, Aditya Puttaparthi
Pubblicazione: (2025)

Scalable Bayesian Clustering for Integrative Analysis of Multi-View Data
di: Cabral, Rafael, et al.
Pubblicazione: (2024)

Model-Free Local Recalibration of Neural Networks
di: Torres, R., et al.
Pubblicazione: (2024)

Statistical inference for rough volatility: Central limit theorems
di: Chong, Carsten, et al.
Pubblicazione: (2022)

Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
di: Redhead, Benjamin R., et al.
Pubblicazione: (2026)

A kernel-based framework for covariate significance tests in nonparametric regression
di: Diz-Castro, Daniel, et al.
Pubblicazione: (2025)

Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
di: Okpala, Izunna, et al.
Pubblicazione: (2025)

Physics-informed machine learning: A mathematical framework with applications to time series forecasting
di: Doumèche, Nathan
Pubblicazione: (2025)

Nonparametric Bayesian volatility learning under microstructure noise
di: Gugushvili, Shota, et al.
Pubblicazione: (2018)

Confounder selection via iterative graph expansion
di: Guo, F. Richard, et al.
Pubblicazione: (2023)

Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices under structural assumptions
di: van der Spek, Rutger, et al.
Pubblicazione: (2022)

Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
di: de Albuquerque, Daniela, et al.
Pubblicazione: (2024)

SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding
di: Kurbucz, Marcell T., et al.
Pubblicazione: (2025)

Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
di: Liu, Lingchong, et al.
Pubblicazione: (2025)

Power Analysis for Experiments with Clustered Data, Ratio Metrics, and Regression for Covariate Adjustment
di: Hesterberg, Tim, et al.
Pubblicazione: (2024)

Martingale Posterior Distributions for Log-concave Density Functions
di: Cui, Fuheng, et al.
Pubblicazione: (2024)

Inference for competing risks based on area between curves statistics
di: Mack, Simon, et al.
Pubblicazione: (2026)

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments
di: Krzeminska, Izabella, et al.
Pubblicazione: (2026)

GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis
di: Heinrichs, Florian
Pubblicazione: (2024)

Bootstrapping not under the null?
di: Derumigny, Alexis, et al.
Pubblicazione: (2025)

American Options Pricing under Heston Model via Curriculum Learning in Coupled PINNs
di: Rohan, et al.
Pubblicazione: (2026)

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
di: Galvão, Luciano Ribeiro, et al.
Pubblicazione: (2025)

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
di: Ji, Wenlong, et al.
Pubblicazione: (2023)

seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees
di: Gnettner, Felix, et al.
Pubblicazione: (2025)

Data-Driven Discovery of Feature Groups in Clinical Time Series
di: Sergeev, Fedor, et al.
Pubblicazione: (2025)

Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI
di: Abtahi, Farhad, et al.
Pubblicazione: (2025)

A Bayesian Bootstrap for Mixture Models
di: Cui, Fuheng, et al.
Pubblicazione: (2023)

Intra-tree Column Subsampling Hinders XGBoost Learning of Ratio-like Interactions
di: Pinchuk, Mykola
Pubblicazione: (2026)

Incorporating Recklessness to Collaborative Filtering based Recommender Systems
di: Pérez-López, Diego, et al.
Pubblicazione: (2023)

Nonparametric Inference on Dose-Response Curves Without the Positivity Condition
di: Zhang, Yikun, et al.
Pubblicazione: (2024)

Functional Clustering of Discount Functions for Behavioral Investor Profiling
di: Porreca, Annamaria, et al.
Pubblicazione: (2024)