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学者姓名:程维虎

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< Page ,Total 13 >
Statistical Inference and Application of Asymmetrical Generalized Pareto Distribution Based on Peaks-Over-Threshold Model SCIE
期刊论文 | 2024 , 16 (3) | SYMMETRY-BASEL
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Abstract :

Generalized Pareto distribution (GPD), an asymmetrical distribution, primarily models exceedances over a high threshold in many applications. Within the peaks-over-threshold (POT) framework, we consider a new GPD parameter estimation method to estimate a common tail risk measure, the value at risk (VaR). The proposed method is more suitable for the POT framework and makes full use of data information. Specifically, our estimation method builds upon the generalized probability weighted moments method and integrates it with the nonlinear weighted least squares method. We use exceedances for the GPD, minimizing the sum of squared differences between the sample and population moments of a function of GPD random variables. At the same time, the proposed estimator uses three iterations and assigns weight to further improving the estimated performance. Under Monte Carlo simulations and with a real heavy-tailed dataset, the simulation results show the advantage of the newly proposed estimator, particularly when VaRs are at high confidence levels. In addition, by simulating other heavy-tailed distributions, our method still exhibits good performance in estimating misjudgment distributions.

Keyword :

extreme value theory extreme value theory extreme quantile estimation extreme quantile estimation parameter estimation parameter estimation generalized Pareto distribution generalized Pareto distribution

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GB/T 7714 Chen, Wenru , Zhao, Xu , Zhou, Mi et al. Statistical Inference and Application of Asymmetrical Generalized Pareto Distribution Based on Peaks-Over-Threshold Model [J]. | SYMMETRY-BASEL , 2024 , 16 (3) .
MLA Chen, Wenru et al. "Statistical Inference and Application of Asymmetrical Generalized Pareto Distribution Based on Peaks-Over-Threshold Model" . | SYMMETRY-BASEL 16 . 3 (2024) .
APA Chen, Wenru , Zhao, Xu , Zhou, Mi , Chen, Haiqing , Ji, Qingqing , Cheng, Weihu . Statistical Inference and Application of Asymmetrical Generalized Pareto Distribution Based on Peaks-Over-Threshold Model . | SYMMETRY-BASEL , 2024 , 16 (3) .
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A novel finite mixture model based on the generalized scale mixtures of asymmetric generalized normal distributions: properties, estimation methodology and applications SCIE
期刊论文 | 2024 | COMPUTATIONAL STATISTICS
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Abstract :

In this paper, we introduce a family of distributions known as generalized scale mixtures of asymmetric generalized normal distributions (GSMAGN), characterized by remarkable flexibility in shape. We propose a novel finite mixture model based on this distribution family, offering an effective tool for modeling intricate data featuring skewness, heavy tails, and multi-modality. To facilitate parameter estimation for this model, we devise an ECM-PLA ensemble algorithm that combines the Profile Likelihood Approach (PLA) with the classical Expectation Conditional Maximization (ECM) algorithm. By incorporating analytical expressions in the E-step and manageable computations in the M-step, this approach significantly enhances computational speed and overall efficiency. Furthermore, we persent the closed-form expressions for the observed information matrix, which serves as an approximation for the asymptotic covariance matrix of the maximum likelihood estimates. Additionally, we expound upon the corresponding consistency characteristics inherent to this particular mixture model. The applicability of the proposed model is elucidated through several simulation studies and practical datasets.

Keyword :

Generalized scale mixtures Generalized scale mixtures Finite mixture model Finite mixture model EM-type algorithm EM-type algorithm Model based clustering Model based clustering Asymmetric generalized normal distribution Asymmetric generalized normal distribution

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GB/T 7714 Guan, Ruijie , Jiao, Junjun , Cheng, Weihu et al. A novel finite mixture model based on the generalized scale mixtures of asymmetric generalized normal distributions: properties, estimation methodology and applications [J]. | COMPUTATIONAL STATISTICS , 2024 .
MLA Guan, Ruijie et al. "A novel finite mixture model based on the generalized scale mixtures of asymmetric generalized normal distributions: properties, estimation methodology and applications" . | COMPUTATIONAL STATISTICS (2024) .
APA Guan, Ruijie , Jiao, Junjun , Cheng, Weihu , Hu, Guozhi . A novel finite mixture model based on the generalized scale mixtures of asymmetric generalized normal distributions: properties, estimation methodology and applications . | COMPUTATIONAL STATISTICS , 2024 .
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Inference and diagnostics for censored linear regression model with skewed generalized t distribution SCIE
期刊论文 | 2024 | JOURNAL OF APPLIED STATISTICS
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Abstract :

In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted.

Keyword :

EM-type algorithms EM-type algorithms residual analysis residual analysis influence analysis influence analysis skewed generalized t distribution skewed generalized t distribution Censored regression model Censored regression model

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GB/T 7714 Lian, Chengdi , Rong, Yaohua , Liang, Jinwen et al. Inference and diagnostics for censored linear regression model with skewed generalized t distribution [J]. | JOURNAL OF APPLIED STATISTICS , 2024 .
MLA Lian, Chengdi et al. "Inference and diagnostics for censored linear regression model with skewed generalized t distribution" . | JOURNAL OF APPLIED STATISTICS (2024) .
APA Lian, Chengdi , Rong, Yaohua , Liang, Jinwen , Guan, Ruijie , Cheng, Weihu . Inference and diagnostics for censored linear regression model with skewed generalized t distribution . | JOURNAL OF APPLIED STATISTICS , 2024 .
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Statistical Reliability Assessment with Generalized Intuitionistic Fuzzy Burr XII Distribution SCIE
期刊论文 | 2024 , 12 (5) | PROCESSES
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Abstract :

Intuitionistic fuzzy sets provide a viable framework for modelling lifetime distribution characteristics, particularly in scenarios with measurement imprecision. This is accomplished by utilizing membership and non-membership degrees to accurately express the complexities of data uncertainty. Nonetheless, the complexities of some cases necessitate a more advanced approach of imprecise data, motivating the use of generalized intuitionistic fuzzy sets (GenIFSs). The use of GenIFSs represents a flexible modeling strategy that is characterized by the careful incorporation of an extra level of hesitancy, which effectively clarifies the underlying ambiguity and uncertainty present in reliability evaluations. The study employs a methodology based on generalized intuitionistic fuzzy distributions to thoroughly examine the uncertainty related to the parameters and reliability characteristics present in the Burr XII distribution. The goal is to provide a more accurate evaluation of reliability measurements by addressing the inherent ambiguity in the distribution's shape parameter. Various reliability measurements, such as reliability, hazard rate, and conditional reliability functions, are derived for the Burr XII distribution. This extensive analysis is carried out within the context of the generalized intuitionistic fuzzy sets paradigm, improving the understanding of the Burr XII distribution's reliability measurements and providing important insights into its performance for the study of various types of systems. To facilitate understanding and point to practical application, the findings are shown graphically and contrasted across various cut-set values using a valuable numerical example.

Keyword :

alpha,beta-cut sets alpha,beta-cut sets generalized intuitionistic fuzzy probability (GenIFP) generalized intuitionistic fuzzy probability (GenIFP) new type generalized intuitionistic fuzzy set (GenIFS) new type generalized intuitionistic fuzzy set (GenIFS) generalized intuitionistic fuzzy reliability characteristics (GenIFRCs) generalized intuitionistic fuzzy reliability characteristics (GenIFRCs) Burr XII distribution Burr XII distribution

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GB/T 7714 Kalam, Abdul , Cheng, Weihu , Stefanatos, Dionisis et al. Statistical Reliability Assessment with Generalized Intuitionistic Fuzzy Burr XII Distribution [J]. | PROCESSES , 2024 , 12 (5) .
MLA Kalam, Abdul et al. "Statistical Reliability Assessment with Generalized Intuitionistic Fuzzy Burr XII Distribution" . | PROCESSES 12 . 5 (2024) .
APA Kalam, Abdul , Cheng, Weihu , Stefanatos, Dionisis , Shah, Sayed Kifayat . Statistical Reliability Assessment with Generalized Intuitionistic Fuzzy Burr XII Distribution . | PROCESSES , 2024 , 12 (5) .
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Change point detection for a skew normal distribution based on the Q-function SCIE
期刊论文 | 2024 , 9 (10) , 28698-28721 | AIMS MATHEMATICS
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Abstract :

In this paper, we enhanced change point detection in skew normal distribution models by integrating the EM algorithm's Q-function with the modified information criterion (MIC). The new QMIC framework improves sensitivity and accuracy in detecting changes, outperforming the modified information criterion (MIC) and the traditional Bayesian information criterion (BIC). Due to the complexity of deriving analytic asymptotic distributions, bootstrap simulations were used to determine critical values at various significance levels. Extensive simulations demonstrate that QMIC offers superior detection capabilities. We applied the QMIC method to two stock market datasets, successfully identifying multiple change points, and highlighting its effectiveness for real- world financial data analysis.

Keyword :

binary segmentation method binary segmentation method expectation maximization expectation maximization skew normal distribution skew normal distribution change point detection change point detection Q-function Q-function

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GB/T 7714 Du, Yang , Cheng, Weihu . Change point detection for a skew normal distribution based on the Q-function [J]. | AIMS MATHEMATICS , 2024 , 9 (10) : 28698-28721 .
MLA Du, Yang et al. "Change point detection for a skew normal distribution based on the Q-function" . | AIMS MATHEMATICS 9 . 10 (2024) : 28698-28721 .
APA Du, Yang , Cheng, Weihu . Change point detection for a skew normal distribution based on the Q-function . | AIMS MATHEMATICS , 2024 , 9 (10) , 28698-28721 .
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The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates SCIE
期刊论文 | 2023 , 11 (20) | MATHEMATICS
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Abstract :

In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets yield consistent estimates from conditional logit estimators for binary response panel data models. When faced with a missing covariates problem, researchers adopt various imputation techniques to complete the data and without loss of generality; consistent estimates still suffice asymptotically. For maximum likelihood estimates of the parameters for logistic regression in cases of imputed covariates, the optimal choice of an imputation technique that yields the best estimates with minimum variance is still elusive. This paper aims to examine the behaviour of the Hessian matrix with optimal values of the imputed covariates vector, which will make the Newton-Raphson algorithm converge faster through a reduced absolute value of the product of the score function and the inverse fisher information component. We focus on a method used to modify the conditional likelihood function through the partitioning of the covariate matrix. We also confirm that the positive moduli of the Hessian for conditional estimators are sufficient for the concavity of the log-likelihood function, resulting in optimum parameter estimates. An increased Hessian modulus ensures the faster convergence of the parameter estimates. Simulation results reveal that model-based imputations perform better than classical imputation techniques, yielding estimates with smaller bias and higher precision for the conditional maximum likelihood estimation of nonlinear panel models.

Keyword :

Hessian matrix Hessian matrix fixed effects fixed effects maximum likelihood maximum likelihood conditional logit conditional logit

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GB/T 7714 Otieno, Opeyo Peter , Cheng, Weihu . The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates [J]. | MATHEMATICS , 2023 , 11 (20) .
MLA Otieno, Opeyo Peter et al. "The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates" . | MATHEMATICS 11 . 20 (2023) .
APA Otieno, Opeyo Peter , Cheng, Weihu . The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates . | MATHEMATICS , 2023 , 11 (20) .
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Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses SCIE
期刊论文 | 2023 , 11 (8) | MATHEMATICS
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In this paper, we propose a model averaging estimation for the varying-coefficient partially linear models with missing responses. Within this context, we construct a HRCp weight choice criterion that exhibits asymptotic optimality under certain assumptions. Our model averaging procedure can simultaneously address the uncertainty on which covariates to include and the uncertainty on whether a covariate should enter the linear or nonlinear component of the model. The simulation results in comparison with some related strategies strongly favor our proposal. A real dataset is analyzed to illustrate the practical application as well.

Keyword :

model averaging model averaging HRCp HRCp asymptotic optimality asymptotic optimality missing data missing data varying-coefficient partially linear model varying-coefficient partially linear model

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GB/T 7714 Zeng, Jie , Cheng, Weihu , Hu, Guozhi . Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses [J]. | MATHEMATICS , 2023 , 11 (8) .
MLA Zeng, Jie et al. "Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses" . | MATHEMATICS 11 . 8 (2023) .
APA Zeng, Jie , Cheng, Weihu , Hu, Guozhi . Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses . | MATHEMATICS , 2023 , 11 (8) .
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Tolerance Limits Under Gamma Mixtures: Application in Hydrology SCIE
期刊论文 | 2023 , 36 (3) , 1285-1301 | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
WoS CC Cited Count: 1
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Abstract :

In this study, the authors proposed upper tolerance limits for the gamma mixture distribution based on generalized fiducial inference, and an MCMC simulation is performed to sample from the generalized fiducial distributions. The simulation results and a real hydrological data example show that the proposed tolerance limits are more efficient.

Keyword :

Markov chain Monte Carlo Markov chain Monte Carlo incomplete data incomplete data latent variable latent variable generalized fiducial inference generalized fiducial inference Gamma mixture distribution Gamma mixture distribution

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GB/T 7714 Jiao, Junjun , Cheng, Weihu . Tolerance Limits Under Gamma Mixtures: Application in Hydrology [J]. | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2023 , 36 (3) : 1285-1301 .
MLA Jiao, Junjun et al. "Tolerance Limits Under Gamma Mixtures: Application in Hydrology" . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 36 . 3 (2023) : 1285-1301 .
APA Jiao, Junjun , Cheng, Weihu . Tolerance Limits Under Gamma Mixtures: Application in Hydrology . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2023 , 36 (3) , 1285-1301 .
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Content-adjusted tolerance intervals via bootstrap calibration SCIE
期刊论文 | 2022 , 11 (1) | STAT
WoS CC Cited Count: 1
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Abstract :

Tolerance intervals (TIs) are commonly employed in numerous industries, ranging from engineering to pharmaceuticals. However, closed-form TIs are unavailable for most distributions. Although some approximate methods can be used to obtain TIs, coverage probabilities (CPs) of these TIs cannot achieve the nominal level, or can be even far different from the nominal level. In this study, we propose two content-adjusted procedures for TIs based on bootstrap. The first one is based on the bootstrap sample quantile, while the second one is based on the asymptotic normality of empirical distribution. The simulation results show that the two calibration procedures can improve CPs of TIs for some non-normal distributions according to extensive numerical simulations, and they are both proved to be effective through real data examples.

Keyword :

non-normal distributions non-normal distributions content correction content correction tolerance intervals tolerance intervals bootstrap calibration bootstrap calibration calibration interval calibration interval

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GB/T 7714 Jiao, Junjun , Zhao, Xu , Cheng, Weihu . Content-adjusted tolerance intervals via bootstrap calibration [J]. | STAT , 2022 , 11 (1) .
MLA Jiao, Junjun et al. "Content-adjusted tolerance intervals via bootstrap calibration" . | STAT 11 . 1 (2022) .
APA Jiao, Junjun , Zhao, Xu , Cheng, Weihu . Content-adjusted tolerance intervals via bootstrap calibration . | STAT , 2022 , 11 (1) .
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Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors SCIE SSCI
期刊论文 | 2022 , 10 (9) | MATHEMATICS
WoS CC Cited Count: 4
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Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM 2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM 2.5 concentration, achieving ideal results.

Keyword :

generalized Pareto distribution generalized Pareto distribution long short-term memory long short-term memory dynamic conditional autoregressive modeling dynamic conditional autoregressive modeling peaks over threshold peaks over threshold threshold selection threshold selection

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GB/T 7714 Huang, Chunli , Zhao, Xu , Cheng, Weihu et al. Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors [J]. | MATHEMATICS , 2022 , 10 (9) .
MLA Huang, Chunli et al. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors" . | MATHEMATICS 10 . 9 (2022) .
APA Huang, Chunli , Zhao, Xu , Cheng, Weihu , Ji, Qingqing , Duan, Qiao , Han, Yufei . Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors . | MATHEMATICS , 2022 , 10 (9) .
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