## 33 Citations

### Bayesian joint inference for multivariate quantile regression model with L 1 / 2 penalty

- Mathematics
- 2021

This paper considers a Bayesian approach for joint estimation of the marginal conditional quantiles from several dependent variables under a linear regression framework. This approach incorporates…

### General linear hypothesis testing in functional response model

- MathematicsCommunications in Statistics - Theory and Methods
- 2019

Abstract In this paper, the general linear hypothesis testing and variable selection problems in functional response model are considered. The globalizing pointwise F test and the -test as well as…

### FDP control in multivariate linear models using the bootstrap

- Mathematics
- 2022

In this article we develop a method for performing post hoc inference of the False Discovery Proportion (FDP) over multiple contrasts of interest in the multivariate linear model. To do so we use the…

### Bootstrapping Statistical Inference for Off-Policy Evaluation

- Computer ScienceArXiv
- 2021

This paper proposes a bootstrapping FQE method for inferring the distribution of the policy evaluation error and shows that this method is asymptotically efficient and distributionally consistent for off-policy statistical inference.

### Correction to: Bayesian joint inference for multivariate quantile regression model with L1/2 penalty

- EducationComputational Statistics
- 2021

Acknowledgements Authors thank editors and referees for their constructive comments and suggestions which have greatly improved the paper. The research of Yu-Zhu Tian was partially supported by…

### Bootstrapping Fitted Q-Evaluation for Off-Policy Inference

- Computer ScienceICML
- 2021

This paper proposes a bootstrapping FQE method for inferring the distribution of the policy evaluation error and shows that this method is asymptotically efﬁcient and distributionally consistent for off-policy statistical inference.

### The Importance of Discussing Assumptions when Teaching Bootstrapping

- Education
- 2021

Bootstrapping and other resampling methods are progressively appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods. Though simple…

### General model-free weighted envelope estimation

- Mathematics
- 2020

Envelope methodology is succinctly pitched as a class of procedures for increasing efficiency in multivariate analyses without altering traditional objectives \citep[first sentence of page…

### Evaluating Uncertainty of Microwave Calibration Models With Regression Residuals

- MathematicsIEEE Transactions on Microwave Theory and Techniques
- 2020

The sensitivity-analysis and Monte Carlo algorithms for evaluating the uncertainty of multivariate microwave calibration models with regression residuals are presented and their limitations in the presence of correlated errors are explored.

## References

SHOWING 1-10 OF 12 REFERENCES

### Building Multiple Regression Models Interactively

- Psychology
- 1981

Automated multiple regression model-building techniques often hide important aspects of data from the data analyst. Such features as nonlinearity, collinearity, outliers, and points with high…

### Bootstrapping a Regression Equation: Some Empirical Results

- Mathematics
- 1984

Abstract The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation based on a nonparametric estimate of the underlying error…

### Bootstrapping Regression Models

- Mathematics
- 2002

Bootstrapping is a general approach to statistical inference based on building a sampling distribution for a statistic by resampling from the data at hand. The term ‘bootstrapping,’ due to Efron…

### Strong consistency of least squares estimates in multiple regression.

- MathematicsProceedings of the National Academy of Sciences of the United States of America
- 1978

### Strong consistency of least squares estimates in multiple regression

- Mathematics
- 1978

The strong consistency of least squares estimates in multiple regression models with independent errors is obtained under minimal assumptions on the design and weak moment conditions on the errors.

### Computer-Intensive Methods in Statistics

- Computer Science
- 1983

The bootstrap method is examined and evaluated as an example of this new generation of statistical tools that take advantage of the high speed digital computer and free the statistician to attack more complicated problems.

### Variance estimation in high dimensional regression models

- Mathematics, Economics
- 2000

We treat the problem of variance estimation of the least squares estimate of the parameter in high dimensional linear regression models by using the Uncor- related Weights Bootstrap ( UBS ). We find…