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Multidisciplinary Sciences
Mohamed Kayid, Abdulrahman Abouammoh, Ghadah Alomani, Dario Ferreira, Calogero Vetro
Summary: Statistical probability models are often used to analyze real-world data in many research fields. However, data from fields such as the environment, economics, and health care may not fit traditional models. This study investigates an extension of the quasi-Lindley model that is asymmetrically distributed on the positive real number line. Various algorithms are used to estimate the parameters, and the results show that all techniques provide accurate and reliable estimates. The proposed model outperforms alternative models when analyzing a reliability dataset.
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Statistics & Probability
Qihui Su, Zhongling Qin, Liang Peng, Gengsheng Qin
Summary: This study introduces an efficient backtest method for risk variables modeled by ARMA-GARCH processes, requiring fewer finite moments for robustness to heavier tails. By adding a constraint on the goodness of fit for the error distribution, more accurate risk forecasts and improved test power are provided. Both simulation and empirical analyses confirm the good performance of the new backtests in monitoring financial crises.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
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Mathematics, Applied
Vu Dinh, Lam Si Tung
Summary: This paper focuses on the convergence rate of the maximum likelihood supertree method and proposes an analytic approach for analyzing it. By treating each tree as a point in a metric space and proving that the distance between the maximum likelihood supertree and the species tree converges to zero at a polynomial rate under certain conditions, the study contributes to understanding the behavior of supertree reconstruction methods.
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Economics
Takaki Sato, Yasumasa Matsuda
Summary: This paper introduces an extension of GARCH models to spatial data, known as S-GARCH models. By re-expressing S-GARCH models as SARMA models and proposing a two-step estimation process based on quasi-likelihood functions, the consistency and asymptotic normality of the parameters are proven. S-GARCH models are applied to simulated and land-price data in Tokyo to illustrate their empirical properties.
SPATIAL ECONOMIC ANALYSIS
(2021)
Article
Statistics & Probability
Yakoub Boularouk
Summary: This paper proposes a consistent estimator for the noise mean of the GARCH process based on Laplace errors. It also proves the consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator of the GARCH model based on Laplace residuals with any mean value. Numerical simulations confirm the accuracy of the estimators.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Mathematics, Interdisciplinary Applications
Mo Zhou, Liang Peng, Rongmao Zhang
Summary: This paper discusses the use of the ARMA-GARCH model in financial econometrics and proposes an empirical likelihood test to address zero mean errors when using the SWQMELE method. Simulation studies confirm the effectiveness of this test before applying it to empirical research.
JOURNAL OF TIME SERIES ANALYSIS
(2021)
Article
Economics
Lei Jiang, Weimin Liu, Liang Peng
Summary: Using ARMA-GARCH models with weighted least-squares estimate and random weighted bootstrap method, more funds with positive timing ability are identified. Empirical evidence suggests that funds with perverse timing ability tend to have high fund turnovers and need to balance between timing and stock picking skills.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Mathematics
Yaoting Yang, Weizhong Tian, Tingting Tong
Summary: A new generalized mixture of exponential distribution is introduced, with its basic properties and estimation methods studied. Simulation studies are conducted to assess the performance of the estimators. Real-world applications show that the new distribution outperforms its competitors.
Article
Statistics & Probability
Ngai Hang Chan, Wai Leong Ng, Chun Yip Yau
Summary: A self-normalization sequential change-point detection method is proposed for time series analysis, aiming to address issues with traditional tests such as sensitivity to bandwidth parameters and size distortion. The null asymptotic and consistency of the proposed method are established under general regularity conditions, and its effectiveness is demonstrated through simulation experiments and application to railway-bearing temperature data.
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Economics
Feiyu Jiang, Dong Li, Ke Zhu
Summary: This paper proposes a S-GARCH model and estimation methods for both long run and short run variance components, as well as hypothesis testing approaches. The results show that the proposed methods have good efficiency and testing power when the S-GARCH model is stationary.
JOURNAL OF ECONOMETRICS
(2021)
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Engineering, Industrial
Coskun Kus, Serkan Eryilmaz
Summary: This paper studies a two-unit standby repairable system using matrix-exponential distributions. The Laplace transform of the system's lifetime is obtained under the assumption of statistically dependent damage size and repair time. The reliability evaluation of the system is performed based on known distributional properties, and the estimation of unknown parameters is discussed using system's lifetime data.
QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT
(2021)
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Statistics & Probability
Zheqi Wang, Dehui Wang
Summary: A covariate-driven random coefficient generalized conditional heteroscedasticity (GARCH) time series model with the form of buffered autoregression (BRC-GARCH-X) is proposed for modeling financial time series data. The model utilizes a more flexible regime-switching mechanism and improves performance by formulating the threshold variable as a weighted average of important auxiliary variables.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Danni Xie, Xin Liang, Ruilin Liang
Summary: In this article, the application of symmetric and asymmetric GARCH models in financial time series analysis is studied, and a method based on self-weighted quasi-maximum likelihood estimation is proposed. The results show that this method performs well in parameter estimation and data fitting.
Article
Mathematics
Suad Alhihi, Maalee Almheidat
Summary: This paper investigates the statistical inference, bias, mean square error, and construction of confidence intervals for Pianka's overlap coefficient in two exponential populations.
Article
Mathematics, Applied
Dayang Dai, Dabuxilatu Wang
Summary: This paper introduces a generalized Liu-type estimator (GLTE) to address the multicollinearity problem in the logistic partially linear regression model. The GLTE is derived using the profile likelihood method and it includes several existing estimators as special cases. The superiority of GLTE over other estimators is demonstrated and optimal choices for biasing parameters are provided. Numerical simulations show that GLTE outperforms existing estimators, and an application on real data is presented.
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Economics
Ke Zhu, Wai Keung Li, Philip L. H. Yu
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2017)
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Economics
Dong Li, Xingfa Zhang, Ke Zhu, Shiqing Ling
JOURNAL OF ECONOMETRICS
(2018)
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Economics
Qiying Wang, Dongsheng Wu, Ke Zhu
JOURNAL OF ECONOMETRICS
(2018)
Editorial Material
Mathematics, Interdisciplinary Applications
Dong Li, Ke Zhu
JOURNAL OF TIME SERIES ANALYSIS
(2020)
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Economics
Feiyu Jiang, Dong Li, Ke Zhu
JOURNAL OF ECONOMETRICS
(2020)
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Statistics & Probability
Ke Zhu
ANNALS OF STATISTICS
(2019)
Article
Economics
Mengya Liu, Fukang Zhu, Ke Zhu
Summary: This article introduces a new family of multifrequency-band tests for the white noise hypothesis, which have chi-square asymptotic null distribution and are suitable for heteroscedastic data. An automatic multifrequency-band test is proposed using a data-driven method to select scales. Simulation studies demonstrate the good size and power performance of these tests.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Economics
Feiyu Jiang, Dong Li, Ke Zhu
Summary: This paper proposes a S-GARCH model and estimation methods for both long run and short run variance components, as well as hypothesis testing approaches. The results show that the proposed methods have good efficiency and testing power when the S-GARCH model is stationary.
JOURNAL OF ECONOMETRICS
(2021)
Article
Economics
Guochang Wang, Ke Zhu, Xiaofeng Shao
Summary: This article introduces a new class of tests to examine whether the error term is a martingale difference sequence in a multivariate time series model with parametric conditional mean, based on the martingale difference divergence matrix (MDDM). The tests are consistent for detecting fixed alternatives and have nontrivial power against local alternatives. Additionally, a wild bootstrap procedure is proposed to approximate critical values for the tests, which is theoretically valid.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Mengya Liu, Fukang Zhu, Ke Zhu
Summary: The proposed model, based on a zero-one-inflated Poisson distribution with autoregressive feedback mechanism, successfully captures air quality data in 30 major cities in China. The model is able to generate rational and informative rankings for these cities.
JOURNAL OF TIME SERIES ANALYSIS
(2022)
Article
Economics
Xuanling Yang, Zhoufan Zhu, Dong Li, Ke Zhu
Summary: We propose a new asset pricing model for big panel return data, learning the conditional distribution of the return using a step distribution function and a new conditional quantile variational autoencoder (CQVAE) network. The CQVAE network utilizes latent factors learned from a VAE network and nonlinear factor loadings from a multi-head network to specify the structure of conditional quantiles. We apply the CQVAE asset pricing model to a 60-year US equity return dataset and find that it outperforms the benchmark conditional autoencoder model in terms of out-of-sample R-2 values and Sharpe ratios.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Article
Business, Finance
Shiqing Ling, Ke Zhu
Summary: This paper investigates the estimation problem of the ARMA model with GARCH noises. The consistency, asymptotic normality, and efficiency of different estimators are demonstrated through theoretical and empirical studies.
JOURNAL OF RISK AND FINANCIAL MANAGEMENT
(2022)
Article
Statistics & Probability
Guochang Wang, Wai Keung Li, Ke Zhu
Summary: This study introduces novel one-sided omnibus tests for independence between two multivariate stationary time series, utilizing the Hilbert-Schmidt independence criterion (HSIC) to analyze the independence between the innovations of the time series. The study establishes the limiting null distributions of the tests under regular conditions and demonstrates the consistency of the HSIC-based tests. The use of a residual bootstrap method for obtaining critical values and the examination of general dependence in contrast to existing linear cross-correlation tests are highlighted as the key contributions of this research.
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Statistics & Probability
Qiying Wang, Ke Zhu
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Economics
Dong Li, Shaojun Guo, Ke Zhu
ECONOMETRIC REVIEWS
(2019)
