arXiv research

New method embeds bipartite graphs into vectors, overcoming nonlinear challenges.

Semiparametric Bayesian networks combine parametric and nonparametric models for flexible data analysis.

New estimators improve sparse semiparametric additive modeling.

Bayesian framework for semiparametric regression of discrete data.

Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a …

PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.

The paper proposes a method to estimate complex models using machine learning.

Deep neural networks improve AFT model for non-linear predictors.

Study on identifying and inferring nonlinear dynamics on unknown networks.

ULFS-KDPE estimates parameters efficiently without influence functions.

Discussing hybrid models in Bayesian networks.

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for n…

Paper develops PGMM framework for debiased inference on nonparametric IV estimators.

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical …

Proposes adversarial method to estimate Riesz representer.

New method for semiparametric bandits reduces regret to optimal levels.

DNA-SE uses deep learning to solve semiparametric problems efficiently.

Deep neural nets improve inference in semiparametric models.

Flexible semiparametric model estimates volatility without strong assumptions.

Knowledge distillation improves model accuracy by mimicking teacher model probabilities.

New MAB algorithm for nonstationary, semiparametric reward models.

Bayesian method estimates QTEs from observational data.

New method uses fractional posteriors for semiparametric inference with improved uncertainty quantification.

We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three as…

Paper proposes a semiparametric model for navigating a ball in a circular maze.

To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a large spatial covariance matrix of the generalized $m$-dependent and $β$-mixing time series (with $J$ variables and $T$…

Proposes a framework for selecting machine learning algorithms in semiparametric models.

Study on statistical inference for nonlinear stochastic approximation with Markovian data.

A semiparametric test evaluates instrument validity and complier characteristics.

Semiparametric STAR model improves mental health data analysis.

New method estimates Gaussian copulas with missing data using EM algorithm.

A debiased score test detects linear structure in semiparametric models.

We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, …

Semiparametric method removes bias in functional bilevel gradient estimation.

New algorithm identifies best arm in semiparametric bandits with near optimal efficiency.

Framework for learning sparse DAGs from data.

New method for valid prediction intervals with coarsened data.

Framework for multi-task learning with semiparametric models and nuisance parameters.

New estimator optimizes black-box model errors in semiparametric estimation.

Bayesian methods improve DiD analysis for ATT estimation.

Probabilistic models can be defined by an energy function, where the probability of each state is proportional to the exponential of the state's negative energy. This paper considers a generalization of energy-based models in which the probability of a state is proportional to an arbitrary positive, strictly decreasing…

Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or "nonparanormal"--for high dimensional inference. Just as additive models extend linear models by replacing linear …

It has been recently shown that numerical semiparametric bounds on the expected payoff of fi- nancial or actuarial instruments can be computed using semidefinite programming. However, this approach has practical limitations. Here we use column generation, a classical optimization technique, to address these limitations…

A new model avoids the PH assumption for right-censored survival data.

Proposes estimators for complex dose-response curves using kernel methods.

Proposes a method for valid inference in GPLSIMs with longitudinal data.

A novel approach combines interpretability and performance in machine learning models.

New method handles missing data using AI for efficient inference.