Related references
Note: Only part of the references are listed.
Article
Economics
Rong Jiang et al.
Summary: Single-index quantile regression models can be used to avoid the curse of dimensionality in nonparametric problems. However, they may suffer from quantile crossing, leading to invalid distribution for the response. This article proposes methods to guarantee noncrossing quantile curves and extends them to composite quantile regression. Simulation studies and real data application demonstrate the advantages of the proposed methods in finite sample performance.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Article
Statistics & Probability
Lan Luo et al.
Summary: This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes. The proposed algorithm recursively renews parameter estimates using current data and summary statistics of historical data, achieving statistical and computational efficiency. Additionally, a method for diagnosing the homogeneity assumption of regression coefficients is proposed, and the existing Lambda architecture is expanded for implementation.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Statistics & Probability
Ying Yang et al.
Summary: This article develops an online nonparametric method to dynamically update the estimates of mean and covariance functions for functional data. The proposed method approximates the future optimal bandwidths by a sequence of dynamically changing candidates and combines the corresponding statistics across blocks to form the updated estimation.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Computer Science, Artificial Intelligence
Kangning Wang et al.
Summary: The paper proposes a novel online renewable quantile regression strategy that updates the resulting estimator with current data and summary statistics of historical data, addressing the challenge of implementing quantile regression in a streaming data environment. The new method is computationally efficient, not storage-intensive, and performs well in numerical experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Economics
Zhanxiong Xu et al.
Summary: This study introduces an efficient estimator by constrainedly weighting information across quantiles, which can eliminate the effect of preliminary estimator and achieve good estimation efficiency simultaneously. Compared to the Cramer-Rao lower bound, the relative efficiency loss of the new estimator has a conservative upper bound close to zero in practical situations. Monte Carlo studies show that the proposed method has substantial efficiency gain and better prediction performance in empirical applications to GDP and inflation rate modeling.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Economics
Qianqian Zhu et al.
Summary: This paper proposes a new conditional heteroskedastic model called quantile double autoregression to address the varying structures and conditional heteroskedasticity in financial time series at different quantile levels. The strict stationarity of the model is derived and self-weighted conditional quantile estimation is suggested. The paper also demonstrates the adequacy of the fitted conditional quantiles through portmanteau tests and examines the performance of the inferential tools through simulation studies.
ECONOMETRIC THEORY
(2022)
Article
Computer Science, Artificial Intelligence
Rong Jiang et al.
Summary: This paper presents an online updating method for quantile regression to handle challenges in model fitting and variable selection with big data arriving in streams. The authors propose renewable optimized objective functions and demonstrate the performance of the proposed methods through simulations and data analysis.
Article
Statistics & Probability
Mingxue Quan et al.
Summary: For nonparametric regression in the streaming setting, a novel one-pass estimator is proposed based on penalized orthogonal basis expansions. A general framework is developed to study the interplay between statistical efficiency and memory consumption of estimators. Numerical studies show that the proposed one-pass estimator is highly efficient and has minimal memory footprints.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Economics
Marcelo Fernandes et al.
Summary: In linear quantile regression, smoothing the objective function leads to better performance in terms of mean squared error and accuracy, as well as the ability to estimate quantile density without being affected by the curse of dimensionality. Additionally, a rule of thumb for choosing the smoothing bandwidth is proposed to approximate the optimal bandwidth effectively. Simulation results confirm the effectiveness of the smoothed quantile regression estimator in finite samples.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2021)
Article
Statistics & Probability
Yuzhi Cai et al.
Summary: The study introduces a novel method for estimating the distribution of financial returns, which performs well in handling changes in model parameters. Empirical analysis on Nasdaq returns demonstrates the robustness and outperformance of this method.
STATISTICAL MODELLING
(2021)
Article
Computer Science, Interdisciplinary Applications
Dilanka S. Dedduwakumara et al.
Summary: This paper introduces a simple and efficient two-step estimator for the four generalized lambda distribution parameters, which allows for the use of bootstrapping to obtain interval estimators for the parameters. The new estimators are evaluated through simulations and applied to various datasets.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2021)
Article
Statistics & Probability
Paolo Frumento et al.
Summary: This article introduces a new method for modeling quantile regression coefficients, applies it to longitudinal data, and proposes a novel penalized fixed-effects estimator.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Statistics & Probability
Lan Luo et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2020)
Article
Biology
Paolo Frumento et al.
Article
Statistics & Probability
Elizabeth D. Schifano et al.
Article
Economics
Yuzhi Cai
ECONOMETRIC REVIEWS
(2016)
Article
Economics
Dong Li et al.
JOURNAL OF ECONOMETRICS
(2015)
Article
Economics
Yuzhi Cai et al.
JOURNAL OF FORECASTING
(2013)
