32 results on '"Wang, Dehui"'
Search Results
2. BRC-GARCH-X model: the empirical evidence in stock returns.
- Author
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Wang, Zheqi and Wang, Dehui
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RATE of return on stocks , *TIME series analysis , *HETEROSCEDASTICITY , *GARCH model - Abstract
A covariate-driven random coefficient generalized conditional heteroscedasticity (GARCH) time series model with the form of the buffered autoregression (BRC-GARCH-X) for modeling financial time series data is considered. As an extension of the classical two-regime threshold process, the buffered autoregression enjoys a more flexible regime-switching mechanism. Furthermore, the main feature of this model is that the threshold variable for regime-switching is formulated as a weighted average of important auxiliary variables. The estimator for regression parameters is obtained by the quasi-maximum exponential likelihood (QMEL) estimator and the corresponding asymptotic properties are established. Moreover, a mixed portmanteau test is developed for diagnostic checking. And a reasonable method for selecting search ranges for thresholds is also proposed and simulation studies are considered. As an application, we bring attention to some features of of stock returns of SP500 which shows that our model is feasible. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
3. Bivariate Random Coefficient Integer-Valued Autoregressive Model Based on a ρ -Thinning Operator.
- Author
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Liu, Chang and Wang, Dehui
- Subjects
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AUTOREGRESSIVE models , *MONTE Carlo method , *ASYMPTOTIC normality , *TIME series analysis , *INTEGERS , *BINOMIAL distribution - Abstract
While overdispersion is a common phenomenon in univariate count time series data, its exploration within bivariate contexts remains limited. To fill this gap, we propose a bivariate integer-valued autoregressive model. The model leverages a modified binomial thinning operator with a dispersion parameter ρ and integrates random coefficients. This approach combines characteristics from both binomial and negative binomial thinning operators, thereby offering a flexible framework capable of generating counting series exhibiting equidispersion, overdispersion, or underdispersion. Notably, our model includes two distinct classes of first-order bivariate geometric integer-valued autoregressive models: one class employs binomial thinning (BVGINAR(1)), and the other adopts negative binomial thinning (BVNGINAR(1)). We establish the stationarity and ergodicity of the model and estimate its parameters using a combination of the Yule–Walker (YW) and conditional maximum likelihood (CML) methods. Furthermore, Monte Carlo simulation experiments are conducted to evaluate the finite sample performances of the proposed estimators across various parameter configurations, and the Anderson-Darling (AD) test is employed to assess the asymptotic normality of the estimators under large sample sizes. Ultimately, we highlight the practical applicability of the examined model by analyzing two real-world datasets on crime counts in New South Wales (NSW) and comparing its performance with other popular overdispersed BINAR(1) models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
4. A class of kth‐order dependence‐driven random coefficient mixed thinning integer‐valued autoregressive process to analyse epileptic seizure data and COVID‐19 data.
- Author
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Liu, Xiufang, Wang, Dehui, Chen, Huaping, Zhao, Lifang, and Liu, Liang
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EPILEPSY , *ASYMPTOTIC normality , *COVID-19 , *AUTOREGRESSIVE models , *TIME series analysis , *ASYMPTOTIC distribution , *AUTOREGRESSION (Statistics) - Abstract
Summary: Data related to the counting of elements of variable character are frequently encountered in time series studies. This paper brings forward a new class of k$$ k $$th‐order dependence‐driven random coefficient mixed thinning integer‐valued autoregressive time series model (DDRCMTINAR(k$$ k $$)) to deal with such data. Stationarity and ergodicity properties of the proposed model are derived in detail. The unknown parameters are estimated by conditional least squares, and modified quasi‐likelihood and asymptotic normality of the obtained parameter estimators is established. The performances of the adopted estimate methods are checked via simulations, which present that modified quasi‐likelihood estimators perform better than the conditional least squares considering the proportion of within‐Ω$$ \Omega $$ estimates in certain regions of the parameter space. The validity and practical utility of the model are investigated by epileptic seizure data and COVID‐19 data of suspected cases in China. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. A new threshold INAR(1) model based on modified negative binomial operator with random coefficient.
- Author
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Fan, Yixuan, Wang, Dehui, and Cheng, Jianhua
- Subjects
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RANDOM operators , *BURGLARY , *TEST methods , *BINOMIAL distribution , *CHRONIC myeloid leukemia , *TIME series analysis , *BINOMIAL theorem - Abstract
In this paper, a new threshold INAR(1) model based on modified negative binomial operator with random coefficient is proposed. Basic probabilistic and statistical properties of this process are established. Then the conditional least squares (CLS) and the conditional maximum likelihood (CML) methods are applied to estimate the model parameters when the threshold value is known or not. The asymptotic properties of the CLS-estimator and the CML-estimator are also been discussed. A method to test the constancy of the autoregressive parameters is provided. As an illustration, a simulation study is conducted to illustrate the performances of these estimators and present an empirical analysis of monthly counts of break and enter non-dwelling in Bellingen. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. A new RCAR(1) model based on explanatory variables and observations.
- Author
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Sheng, Danshu, Wang, Dehui, and Kang, Yao
- Subjects
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QUANTILE regression , *ASYMPTOTIC normality , *RANDOM variables , *TIME series analysis , *MAXIMUM likelihood statistics , *ASYMPTOTIC distribution - Abstract
The random coefficient autoregressive (RCAR) processes are very useful to model time series in applications. It is commonly observed that the random autoregressive coefficient is assumed to be an independent identically distributed (i.i.d.) random variable sequence. To make the RCAR model more practical, this paper considers a new RCAR(1) model driven by explanatory variable and observations. We use the conditional least squares, the quantile regression and the conditional maximum likelihood methods to estimate the model parameters. The consistency and asymptotic normality of the proposed estimates are established. Simulation studies are conducted for the evaluation of the developed approaches and two applications to real-data examples are provided. The results show that the proposed procedures perform well for the simulations and application. [ABSTRACT FROM AUTHOR]
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- 2024
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7. A Two-Step Estimation Method for a Time-Varying INAR Model.
- Author
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Pang, Yuxin, Wang, Dehui, and Goh, Mark
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STOCHASTIC systems , *TIME series analysis , *STOCHASTIC processes , *STATISTICAL models , *ANALYTICAL solutions - Abstract
This paper proposes a new time-varying integer-valued autoregressive (TV-INAR) model with a state vector following a logistic regression structure. Since the autoregressive coefficient in the model is time-dependent, the Kalman-smoothed method is applicable. Some statistical properties of the model are established. To estimate the parameters of the model, a two-step estimation method is proposed. In the first step, the Kalman-smoothed estimation method, which is suitable for handling time-dependent systems and nonstationary stochastic processes, is utilized to estimate the time-varying parameters. In the second step, conditional least squares is used to estimate the parameter in the error term. This proposed method allows estimating the parameters in the nonlinear model and deriving the analytical solutions. The performance of the estimation method is evaluated through simulation studies. The model is then validated using actual time series data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. A new bivariate autoregressive model driven by logistic regression.
- Author
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Wang, Zheqi, Wang, Dehui, and Cheng, Jianhua
- Subjects
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AUTOREGRESSIVE models , *LOGISTIC regression analysis , *STATISTICAL models , *CHRONIC myeloid leukemia , *TIME series analysis , *BIVARIATE analysis , *AUTOREGRESSION (Statistics) - Abstract
In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. On bivariate threshold Poisson integer-valued autoregressive processes.
- Author
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Yang, Kai, Zhao, Yiwei, Li, Han, and Wang, Dehui
- Subjects
MAXIMUM likelihood statistics ,AUTOREGRESSIVE models ,STATISTICAL models ,TIME series analysis ,FORECASTING - Abstract
To capture the bivariate count time series showing piecewise phenomena, we introduce a first-order bivariate threshold Poisson integer-valued autoregressive process. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares and conditional maximum likelihood estimators, as well as their asymptotic properties, are obtained for both the cases that the threshold parameter is known or not. A new algorithm to estimate the threshold parameter of the model is also provided. Moreover, the nonlinearity test and forecasting problems are also addressed. Finally, some numerical results of the estimates and a real data example are presented. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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10. First-order binomial autoregressive processes with Markov-switching coefficients.
- Author
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Yan, Han, Wang, Dehui, and Wang, Zheqi
- Subjects
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AUTOREGRESSIVE models , *MARKOV processes , *MAXIMUM likelihood statistics , *TIME series analysis , *STATISTICAL models , *AUTOREGRESSION (Statistics) , *FORECASTING - Abstract
In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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11. A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables.
- Author
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Zhang, Tongwei, Fu, Lianyan, Wang, Dehui, and Yu, Zhuoxi
- Subjects
GARCH model ,TIME series analysis ,HETEROSCEDASTICITY - Abstract
In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model's parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
12. Estimation of parameters in the MDDRCINAR(p) model.
- Author
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Liu, Xiufang, Jiang, Hao, and Wang, Dehui
- Subjects
LEAST squares ,TIME series analysis ,PARAMETER estimation ,ASYMPTOTIC distribution - Abstract
This paper brings forward a pth-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (MDDRCINAR(p)). Stationarity and ergodicity properties of the proposed model are derived. The unknown parameters are estimated by conditional least squares, weighted least squares and maximum quasi-likelihood and asymptotic characterization of the obtained parameter estimators is proved. The performances of the proposed estimate methods are checked via simulations, which present that maximum quasi-likelihood estimators perform better than the other two estimate methods considering the proportion of within-Ω estimates in certain regions of the parameter space. The applicability of the model is investigated using two real count data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
13. Generalized Poisson integer-valued autoregressive processes with structural changes.
- Author
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Zhang, Chenhui, Wang, Dehui, Yang, Kai, Li, Han, and Wang, Xiaohong
- Subjects
- *
MAXIMUM likelihood statistics , *TIME series analysis , *STATISTICAL models , *AUTOREGRESSIVE models , *POISSON distribution - Abstract
In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
14. Modelling and monitoring of INAR(1) process with geometrically inflated Poisson innovations.
- Author
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Li, Cong, Zhang, Haixiang, and Wang, Dehui
- Subjects
MAXIMUM likelihood statistics ,MOVING average process ,TIME series analysis ,AUTOREGRESSIVE models ,QUALITY control charts ,AUTOREGRESSION (Statistics) - Abstract
To analyse count time series data inflated at the r + 1 values { 0 , 1 , ... , r } , we propose a new first-order integer-valued autoregressive process with r-geometrically inflated Poisson innovations. Some statistical properties together with conditional maximum likelihood estimate are provided. For the purpose of statistical monitoring, we focus on the cumulative sum chart, exponentially weighted moving average chart and combined jumps chart towards the proposed process. Numerical simulations indicate that the conditional maximum likelihood estimator is unbiased. Moreover, the cumulative sum chart is the best choice to monitor our model in practice. Some applications about telephone complaints data are provided to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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15. Maximum likelihood estimation of the DDRCINAR(p) model.
- Author
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Liu, Xiufang, Wang, Dehui, Deng, Dianliang, Cheng, Jianhua, and Lu, Feilong
- Subjects
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MAXIMUM likelihood statistics , *ASYMPTOTIC normality , *POISSON distribution , *TIME series analysis , *ASYMPTOTIC distribution , *BOX-Jenkins forecasting - Abstract
In this paper, the novel estimating methods and their properties for pth-order dependence-driven random coefficient integer-valued autoregressive time series model (DDRCINAR(p)) are studied as the innovation sequence has a Poisson distribution and the thinning is binomial. Strict stationarity and ergodicity for DDRCINAR(p) model are proved. Conditional maximum likelihood and conditional least squares are used to estimate the model parameters. Asymptotic normality of the proposed estimators are derived. Finite sample properties of the conditional maximum likelihood estimator are examined in relation to the widely used conditional least squares estimator. It is concluded that, if the Poisson assumption can be justified, conditional maximum likelihood method performs better in terms of bias and MSE. Finally, three real data sets are analyzed to demonstrate the practical relevance of the model. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
16. Quasi‐maximum exponential likelihood estimation for double‐threshold GARCH models.
- Author
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Zhang, Tongwei, Wang, Dehui, and Yang, Kai
- Subjects
- *
GARCH model , *HANG Seng Index , *TIME series analysis , *HETEROSCEDASTICITY - Abstract
We consider the nonparametric inference for the double‐threshold generalized autoregressive conditional heteroscedastic models. The quasi‐maximum exponential likelihood estimators (QMELEs) of the model parameters are obtained, and their asymptotic properties are established. Simulation studies imply that the estimators are asymptotically normally distributed. An empirical investigation of stock returns illustrates our findings. Both the simulations and the example indicate that the QMELE is feasible, reliable and appropriate to fit the financial time series data of the Hang Seng Index. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
17. Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach.
- Author
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Wang, Xinyang, Wang, Dehui, and Yang, Kai
- Subjects
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TIME series analysis , *PROBLEM solving , *NONPARAMETRIC estimation , *PARAMETER estimation , *COMPUTER simulation - Abstract
This paper proposes a penalized maximum quasi-likelihood (PMQL) estimation that can solve the problem of order selection and parameter estimation regarding the pth-order integer-valued time series models. The PMQL estimation can effectively delete the insignificant orders in model. By contrast, the significant orders can be retained and their corresponding parameters are estimated, simultaneously. Moreover, the PMQL estimation possesses certain robustness hence its order shrinkage effectiveness is superior to the traditional penalized estimation method even if the data is contaminated. The theoretical properties of the PMQL estimator, including the consistency and oracle properties, are also investigated. Numerical simulation results show that our method is effective in a variety of situations. The Westgren's data set is also analyzed to illustrate the practicability of the PMQL method. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
18. Estimation and testing for the integer-valued threshold autoregressive models based on negative binomial thinning.
- Author
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Wang, Xiaohong, Wang, Dehui, Yang, Kai, and Xu, Da
- Subjects
- *
TIME series analysis , *BINOMIAL theorem , *POINT processes , *FIX-point estimation , *CRIME analysis , *TEST methods , *AUTOREGRESSIVE models - Abstract
To better describe the characteristics of time series of counts such as overdispersion or structural change, in this paper, we redefines the integer-valued threshold autoregressive models based on negative binomial thinning (NBTINAR(1)) under a weaker condition that the expectation of the innovations is finite. Parameters' point estimation and interval estimation problems are considered. A method to test the nonlinearity of the data is provided. As an illustration, we conduct a simulation study and empirical analysis of Pittsburgh crime data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
19. A multinomial autoregressive model for finite-range time series of counts.
- Author
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Zhang, Jie, Wang, Dehui, Yang, Kai, and Xu, Yanju
- Subjects
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TIME series analysis , *AUTOREGRESSIVE models , *MULTINOMIAL distribution , *STATISTICAL models , *PARAMETER estimation , *AUTOREGRESSION (Statistics) , *COUNTING - Abstract
In this paper, a multinomial autoregressive model for finite-range time series of counts is introduced to analyze the finite-range integer-valued data with more than two states. Basic probabilistic and statistical properties of the model are discussed. The related estimations of the parameters in proposed model are considered using conditional least squares (CLS), weighted conditional least squares (WCLS) and conditional maximum likelihood (CML) methods. The asymptotic properties of the estimators are established. Some simulation studies are conducted to verify the proposed procedure. A real example is analyzed to illustrate the advantages of our model. • A multinomial autoregressive model for finite-range time series of counts. • Basic probabilistic and statistical properties. • The CLS, WCLS and CML estimations and the related asymptotic properties. • A real data example is investigated to assess the performance. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
20. A study of RCINAR(1) process with generalized negative binomial marginals.
- Author
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Zhang, Jie, Wang, Dehui, and Yang, Kai
- Subjects
- *
TIME series analysis , *AUTOREGRESSIVE models , *VARIANCES , *PARAMETER estimation - Abstract
To better describe the data whose variance is greater than mean in time series analysis, this paper introduces the RCINAR(1) process with generalized negative binomial marginals. The related estimations of this process are considered using Yule-Walker, modified conditional least squares, conditional maximum likelihood and Bayesian methods. The asymptotic properties of the estimators are established. Some simulations are conducted to verify the proposed estimation methods and a real example is proposed to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
21. Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure.
- Author
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Wang, Xinyang, Wang, Dehui, and Zhang, Haixiang
- Subjects
POISSON processes ,AUTOREGRESSIVE models ,AUTOREGRESSION (Statistics) ,HETEROSCEDASTICITY ,TIME series analysis - Abstract
In this paper, we consider the penalized estimation procedure for Poisson autoregressive model with sparse parameter structure. We study the theoretical properties of penalized conditional maximum likelihood (PCML) with several different penalties. We show that the penalized estimators perform as well as the true model was known. We establish the oracle properties of PCML estimators. Some simulation studies are conducted to verify the proposed procedure. A real data example is also provided. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
22. Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes.
- Author
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Yang, Kai, Kang, Yao, Wang, Dehui, Li, Han, and Diao, Yajing
- Subjects
POISSON processes ,AUTOREGRESSIVE models ,MAXIMUM likelihood statistics ,TIME series analysis ,STATISTICAL models ,AUTOREGRESSION (Statistics) ,DATA - Abstract
To accurately and flexibly capture the dispersion features of time series of counts, we introduce the generalized Poisson thinning operation and further define some new integer-valued autoregressive processes. Basic probabilistic and statistical properties of the models are discussed. Conditional least squares and maximum quasi likelihood estimators are investigated via the moment targeting estimation methods for the innovation free case. Also, the asymptotic properties of the estimators are obtained. Conditional maximum likelihood estimation for the parametric cases are also discussed. Finally, some numerical results of the estimates and two real data examples are presented. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
23. Threshold autoregression analysis for finite-range time series of counts with an application on measles data.
- Author
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Yang, Kai, Wang, Dehui, and Li, Han
- Subjects
- *
AUTOREGRESSION (Statistics) , *TIME series analysis , *BINOMIAL distribution , *ALGORITHMS , *SIMULATION methods & models - Abstract
This article studies the threshold autoregression analysis for the self-exciting threshold binomial autoregressive processes. Parameters' point estimation and interval estimation problems are considered via the empirical likelihood method. A new algorithm to estimate the threshold value of the threshold model is also given. Simulation study is conducted for the evaluation of the developed approach. An application on measles data is provided to show the applicability of the method. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
24. Generalized RCINAR(1) Process with Signed Thinning Operator.
- Author
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Zhang, Haixiang, Wang, Dehui, and Zhu, Fukang
- Subjects
- *
GENERALIZABILITY theory , *OPERATOR theory , *AUTOREGRESSION (Statistics) , *TIME series analysis , *MATHEMATICAL models , *PARAMETER estimation , *STATISTICAL bootstrapping , *DATA analysis - Abstract
A generalized random coefficient first-order integer-valued autoregressive process with signed thinning operator is introduced, this kind of process is appropriate for modeling negative integer-valued time series. Strict stationarity and ergodicity of process are established. Estimators of the parameters of interest are derived and their properties are studied via simulation. At last, we use bootstrap method in the real data analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
25. Generalized RCINAR(p) Process with Signed Thinning Operator.
- Author
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Wang, Dehui and Zhang, Haixiang
- Subjects
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TIME series analysis , *NATURAL numbers , *MULTILEVEL models , *AUTOREGRESSION (Statistics) , *ERGODIC theory , *SIMULATION methods & models , *OPERATOR theory - Abstract
We propose a new integer-valued time series process, called generalized pth-order random coefficient integer-valued autoregressive process with signed thinning operator. This kind of process is appropriate for modeling negative integer-valued time series; strict stationarity and ergodicity of the process are established. Estimators of the model's parameters are derived and their properties are studied via simulation. We apply our process to a real data example. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
26. First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts.
- Author
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Zhang, Jie, Wang, Dehui, Yang, Kai, and Dong, Xiaogang
- Subjects
- *
TIME series analysis , *LEAST squares , *MULTINOMIAL distribution , *STATISTICAL models , *MAXIMUM likelihood statistics , *AUTOREGRESSIVE models , *AUTOREGRESSION (Statistics) - Abstract
In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
27. Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes.
- Author
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Liu, Congmin, Cheng, Jianhua, and Wang, Dehui
- Subjects
INFERENTIAL statistics ,LEAST squares ,WORKERS' compensation ,MAXIMUM likelihood statistics ,TIME series analysis ,AUTOREGRESSIVE models - Abstract
This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers' Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
28. Diagnostic checking integer-valued ARCH models using conditional residual autocorrelations
- Author
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Zhu, Fukang and Wang, Dehui
- Subjects
- *
TIME series analysis , *DIAGNOSIS , *ARCH model (Econometrics) , *AUTOCORRELATION (Statistics) , *ASYMPTOTIC expansions , *STATISTICAL hypothesis testing , *ASYMPTOTIC distribution , *MONTE Carlo method , *SIMULATION methods & models - Abstract
Abstract: Time series of counts are commonly observed in real-world applications. The integer-valued ARCH models are able to describe integer-valued processes and offer the potential to be widely applied in practice in future. This paper develops an asymptotic theory for (partial) autocorrelations of the conditional residuals from the integer-valued ARCH model. Based on the above results, we propose five portmanteau test statistics, which are very useful in checking the adequacy of a fitted integer-valued ARCH specification. The asymptotic distributions of the statistics are derived and their finite sample properties are studied in detail through Monte Carlo simulations. Finally, we illustrate the results analyzing two empirical examples. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
29. Monitoring the Zero-Inflated Time Series Model of Counts with Random Coefficient.
- Author
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Li, Cong, Cui, Shuai, Wang, Dehui, and Weiss, Christian H.
- Subjects
TIME series analysis ,SURVIVAL analysis (Biometry) ,CRIME ,STANDARD deviations ,EXPERIMENTAL design - Abstract
In this research, we consider monitoring mean and correlation changes from zero-inflated autocorrelated count data based on the integer-valued time series model with random survival rate. A cumulative sum control chart is constructed due to its efficiency, the corresponding calculation methods of average run length and the standard deviation of the run length are given. Practical guidelines concerning the chart design are investigated. Extensive computations based on designs of experiments are conducted to illustrate the validity of the proposed method. Comparisons with the conventional control charting procedure are also provided. The analysis of the monthly number of drug crimes in the city of Pittsburgh is displayed to illustrate our current method of process monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
30. Estimation of parameters in the self-exciting threshold autoregressive processes for nonlinear time series of counts.
- Author
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Yang, Kai, Li, Han, and Wang, Dehui
- Subjects
- *
TIME series analysis , *NONLINEAR analysis , *AUTOREGRESSIVE models , *MAXIMUM likelihood statistics , *CONFIDENCE intervals - Abstract
To better describe the characteristics of time series of counts such as over-dispersion, asymmetry and structural change, this paper considers a class of integer-valued self-exciting threshold autoregressive processes that properly capture flexible asymmetric and nonlinear responses without assuming the distributions for the errors. Empirical likelihood methods are proposed for constructing confidence intervals for the parameters of interest. Maximum empirical likelihood estimators, as well as their asymptotic properties, are obtained for both the cases that the threshold variable is known or not. A method to test the nonlinearity of the data is provided. As an illustration, we conduct a simulation study and empirical analysis of Pittsburgh crime data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
31. First-order random coefficient mixed-thinning integer-valued autoregressive model.
- Author
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Chang, Leiya, Liu, Xiufang, Wang, Dehui, Jing, Yingchuan, and Li, Chenlong
- Subjects
- *
TIME series analysis , *ASYMPTOTIC distribution , *BURGLARY , *AUTOREGRESSIVE models , *INTEGERS - Abstract
The paper develops a first-order random coefficient mixed-thinning integer-valued autoregressive time series model (RCMTINAR(1)) to deal with the data related to the counting of elements of variable character. Moments and autocovariance functions for this model are studied as the distribution of the innovation sequence is unknown. The conditional least squares and modified quasi-likelihood are adopted to estimate the model parameters. Asymptotic properties of the obtained estimators are established. The performances of these estimators are investigated and compared with false modified quasi-likelihood via simulations. Finally, the practical relevance of the model is illustrated by using two applications to a SIMpass data set and a burglary data set with a comparison with relevant models that exist so far in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
32. A mixture integer-valued ARCH model
- Author
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Zhu, Fukang, Li, Qi, and Wang, Dehui
- Subjects
- *
TIME series analysis , *EXPECTATION-maximization algorithms , *AUTOCORRELATION (Statistics) , *STATIONARY processes , *PARAMETER estimation , *CRITERION (Theory of knowledge) , *SIMULATION methods & models - Abstract
Abstract: We propose a mixture integer-valued ARCH model for modeling integer-valued time series with overdispersion. The model consists of a mixture of K stationary or non-stationary integer-valued ARCH components. The advantages of the mixture model over the single-component model include the ability to handle multimodality and non-stationary components. The necessary and sufficient first- and second-order stationarity conditions, the necessary arbitrary-order stationarity conditions, and the autocorrelation function are derived. The estimation of parameters is done through an EM algorithm, and the model is selected by three information criterions, whose performances are studied via simulations. Finally, the model is applied to a real dataset. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
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