# G-HIVE: Parameter Estimation and Approximate Inference for Multivariate Response Generalized Linear Models with Hidden Variables

## TL;DR

This paper introduces G-HIVE, a unified framework for parameter estimation and approximate inference in multivariate response GLMs with hidden variables, addressing challenges in non-linear models.

## Contribution

It proposes a novel quasi-likelihood method with bias correction and an approximate inference framework for models with hidden variables, extending existing linear regression approaches.

## Key findings

- G-HIVE effectively estimates parameters in complex models.
- The method reduces bias and improves inference accuracy.
- Simulations and real data show G-HIVE outperforms baseline methods.

## Abstract

In practice, there often exist unobserved variables, also termed hidden variables, associated with both the response and covariates. Existing works in the literature mostly focus on linear regression with hidden variables. However, when the regression model is non-linear, the presence of hidden variables leads to new challenges in parameter identification, estimation, and statistical inference. This paper studies multivariate response generalized linear models (GLMs) with hidden variables. We propose a unified framework for parameter estimation and statistical inference called G-HIVE, short for 'G'eneralized - 'HI'dden 'V'ariable adjusted 'E'stimation. Specifically, based on factor model assumptions, we propose a modified quasi-likelihood approach to estimate an intermediate parameter, defined through a set of reweighted estimating equations. The key of our approach is to construct the proper weight, so that the first-order asymptotic bias of the estimator can be removed by orthogonal projection. Moreover, we propose an approximate inference framework for uncertainty quantification. Theoretically, we establish the first-order and second-order asymptotic bias and the convergence rate of our estimator. In addition, we characterize the accuracy of the Gaussian approximation of our estimator via the Berry-Esseen bound, which justifies the validity of the proposed approximate inference approach. Extensive simulations and real data analysis results show that G-HIVE is feasibly implementable and can outperform the baseline method that ignores hidden variables.