On Statistical Inference for High-Dimensional Binary Time Series
The analysis of non-real-valued data, such as binary time series, has attracted great interest in recent years. This manuscript proposes a post-selection estimator for estimating the coefficient matrices of a high-dimensional generalized binary vector autoregressive process and establishes a Gaussian approximation theorem for the proposed estimator. Furthermore, it introduces a second-order wild bootstrap algorithm to enable statistical inference on the coefficient matrices. Numerical studies and empirical applications demonstrate the good finite-sample performance of the proposed method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sharp Asymptotics and Optimal Performance for Inference in Binary Models
We study convex empirical risk minimization for high-dimensional inference in binary models. Our first result sharply predicts the statistical performance of such estimators in the linear asymptotic regime under isotropi…
Distributed Estimation and Inference for Semi-parametric Binary Response Models
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper studies the maximum score estimator of …
Distributed ComputingTriple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
This study investigates the estimation and the statistical inference about Conditional Average Treatment Effects (CATEs), which have garnered attention as a metric representing individualized causal effects. In our data-…
regressionA strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation
In statistical inference problems, we wish to obtain lower bounds on the minimax risk, that is to bound the performance of any possible estimator. A standard technique to obtain risk lower bounds involves the use of Fano…
Active Learningcompressed sensingDensity EstimationTwo-sample testingFast Supervised Hashing with Decision Trees for High-Dimensional Data
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated the advantage over linear on…
RetrievalVocal Bursts Intensity Prediction