DeepAI AI Chat
Log In Sign Up

Selective Confidence Intervals for Martingale Regression Model

by   Ka Wai Tsang, et al.
The Chinese University of Hong Kong, Shenzhen

In this paper we consider the problem of constructing confidence intervals for coefficients of martingale regression models (in particular, time series models) after variable selection. Although constructing confidence intervals are common practice in statistical analysis, it is challenging in our framework due to the data-dependence of the selected model and the correlation among the variables being selected and not selected. We first introduce estimators for the selected coefficients and show that it is consistent under martingale regression model, in which the observations can be dependent and the errors can be heteroskedastic. Then we use the estimators together with a resampling approach to construct confidence intervals. Our simulation results show that our approach outperforms other existing approaches in various data structures.


page 1

page 2

page 3

page 4


Confidence intervals for high-dimensional Cox models

The purpose of this paper is to construct confidence intervals for the r...

New robust confidence intervals for the mean under dependence

The goal of this paper is to indicate a new method for constructing norm...

Evaluating methods for Lasso selective inference in biomedical research by a comparative simulation study

Variable selection for regression models plays a key role in the analysi...

Valid Inference Corrected for Outlier Removal

Ordinary least square (OLS) estimation of a linear regression model is w...

Improving the replicability of results from a single psychological experiment

We identify two aspects of selective inference as major obstacles for re...

Fisher transformation based Confidence Intervals of Correlations in Fixed- and Random-Effects Meta-Analysis

Meta-analyses of correlation coefficients are an important technique to ...

Inference in High-dimensional Linear Regression

We develop an approach to inference in a linear regression model when th...