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An Analysis of the Problems and Causes of the Formation of Multicultural Families in Korea 2010-2020
Heejung Seo-Reich(Heejung Seo-Reich ),Sun Yiwei(Yiwei Sun),Yu Yinxia(Yinxia Yu),Jin Liuxi(Liuxi Jin),Wang Dehui(Dehui Wang) 영남퇴계학연구원 2022 The Journal of Toegye Studies Vol.5 No.2
This article aims to examine the status of multicultural families in Korea from 2010 to 2020, while analyzing their problems and reasons from a multi-layered perspective, to provide an academic foundation for reference in supporting a social safety system for the stable settlement of those families. Although foreign immigrants experience a certain acceptance due to economic necessity, this attitude is often only a conditional and partial acceptance that does not consider migrants as full members of the community and, moreover, sometimes even suspects them of promoting social division through their presence. This study comprehensively analyzes the current status, problems, and causes of problems in multicultural families in Korea through related data between 2010 and 2020. The problems of multicultural families are manifested mainly in economic, cultural and social terms, and each of these problems is organically interconnected. International marriages between Korean men and female immigrants from developing countries are facing a special set of issues such as unequal status of the spouses or specific problems in child’s education. These problems are serious in the sense that they are not only a temporary burden for the multicultural families, but can also affect future generations, the chances of their development and their inclusion in society. Analysis of these problems, which lie in factors of the social environment and cultural differences, can serve as a basis for countermeasures that will benefit multicultural families in Korea. The problems of multinational families must be understood and addressed in their intertwined complexity in order to find solutions that benefit not only these families, but ultimately society as a whole.
Zheng Liu,Jianjun Chen,Lei Cheng,Huabin Li,Shixi Liu,Hongfei Lou,Jianbo Shi,Ying Sun,Dehui Wang,Chengshuo Wang,Xiangdong Wang,Yongxiang Wei,Weiping Wen,Pingchang Yang,Qintai Yang,Gehua Zhang,Yuan Zhan 대한천식알레르기학회 2020 Allergy, Asthma & Immunology Research Vol.12 No.2
The current document is based on a consensus reached by a panel of experts from the Chinese Society of Allergy and the Chinese Society of Otorhinolaryngology-Head and Neck Surgery, Rhinology Group. Chronic rhinosinusitis (CRS) affects approximately 8% of Chinese adults. The inflammatory and remodeling mechanisms of CRS in the Chinese population differ from those observed in the populations of European descent. Recently, precision medicine has been used to treat inflammation by targeting key biomarkers that are involved in the process. However, there are no CRS guidelines or a consensus available from China that can be shared with the international academia. The guidelines presented in this paper cover the epidemiology, economic burden, genetics and epigenetics, mechanisms, phenotypes and endotypes, diagnosis and differential diagnosis, management, and the current status of CRS in China. These guidelines—with a focus on China—will improve the abilities of clinical and medical staff during the treatment of CRS. Additionally, they will help international agencies in improving the verification of CRS endotypes, mapping of eosinophilic shifts, the identification of suitable biomarkers for endotyping, and predicting responses to therapies. In conclusion, these guidelines will help select therapies, such as pharmacotherapy, surgical approaches and innovative biotherapeutics, which are tailored to each of the individual CRS endotypes.
Flexible INAR(1) models for equidispersed, underdispersed or overdispersed counts
Kang Yao,Wang Dehui,Lu Feilong,Wang Shuhui 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.4
Equidispersed, underdispersed and overdispersed count data are commonly encountered in practice. To better describe these data characteristics, this paper develops two classes of INAR(1) processes, which not only can model a wide range of overdispersion and underdispersion, but also have ability to describe the zero-inflated and zero-deflated characteristics of the count data. The probabilistic and statistical properties of the two processes are studied. Estimators of the model parameters are derived by using conditional maximum likelihood (CML) and modified conditional least squares (MCLS) methods. Some asymptotic properties and numerical results of the estimators are investigated. Three real examples are given to show the flexibility and usefulness of the proposed models.
Empirical likelihood inference for INAR(1) model with explanatory variables
Xue Ding,Dehui Wang 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.4
The integer autoregressive (INAR) model defined through the thinning operator can be used to model many count data in applications. Usually, the autoregressive parameter in the thinning operator is assumed to be constant or random variable varying in [0,1]. To make the INAR model more practical, in this paper we consider the INAR(1) model with explanatory variables incorporated into the autoregressive parameter. We use the empirical likelihood method to investigate this model. The empirical likelihood ratio statistic is derived and its asymptotic distribution is shown to be a chi-squared distribution. The maximum empirical likelihood estimator for the parameters is also given and its asymptotic properties are established. Simulation study is conducted for the evaluation of the developed approach and an application to a real data example is provided.
Bivariate zero truncated Poisson INAR(1) process
Yan Liu,Dehui Wang,Haixiang Zhang,Ningzhong Shi 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.2
In this paper, we propose a new stationary bivariate first order integer-valued autoregressive (BINAR(1)) process with zero truncated Poisson marginal distribution. Some properties about this process are considered, such as probability generating function, autocorrelations, expectations and covariance matrix under conditional and unconditional situation. We also establish the strict stationarity and ergodicity of the process. Estimators of unknown parameters are derived by using Yule–Walker, conditional least squares and maximum likelihood methods. The performance of the proposed estimation procedures are evaluated through Monte Carlo simulations. An application to a real data example is also provided.
On a perturbed MAP risk model under a threshold dividend strategy
Jianhua Cheng,Dehui Wang 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.4
In this paper, we consider a perturbed risk model where the claims arrive according toa Markovian arrival process (MAP) under a threshold dividend strategy. We derive theintegro-differential equations for the Gerber–Shiu expected discounted penalty functionand the moments of total dividend payments until ruin, obtain the analytical solutionsto these equations, and give numerical examples to illustrate our main results. We alsoget a matrix renewal equation for the Gerber–Shiu function, and present some asymptoticformulas for the Gerber–Shiu function when the claim size distributions are heavy-tailed.
Empirical likelihood for linear and log-linear INGARCH models
Fukang Zhu,Dehui Wang 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.1
The integer-valued GARCH model is a popular tool for modeling time series of counts. Thispaper develops empirical likelihood methods for the linear and log-linear integer-valuedGARCH models, respectively.Weconsider three aspects of the empirical likelihood method:estimates, confidence regions and test. For both models, an empirical log-likelihood ratiostatistic is derived and its asymptotic distribution is shown to be a chi-squared distribution. Simulation studies lead to superior performance of the empirical likelihood methodcompared with the conventional treatments in the literature. An application to a real dataexample is also provided.
Regularized estimation in GINAR(p) process
Haixiang Zhang,Dehui Wang,Liuquan Sun 한국통계학회 2017 Journal of the Korean Statistical Society Vol.46 No.4
This article is concerned with the regularized estimation methodology for generalized pthorder integer-valued autoregressive (GINAR(p)) process, especially when the regression coefficients are sparse. Under some mild regularity conditions, we show that the regularized estimators perform as well as if the correct submodel was known. The oracle properties of the estimators are established. Extensive Monte Carlo simulation studies demonstrate that the proposed procedure works well. To illustrate its usefulness, an application to a real data about epileptic patient is also provided.
Cong Li,Dehui Wang,Haixiang Zhang 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.2
To model zero-inflated time series of counts, we propose a first-order mixed integer-valued autoregressive process with zero-inflated generalized power series innovations. These innovations contain the commonly used zero-inflated Poisson and geometric distributions. Strict stationarity, ergodicity of the process, and some important probabilistic properties such as the transition probabilities, the k-step ahead conditional mean and variance are obtained. The conditional maximum likelihood estimators for the parameters in this process are derived and the performances of the estimators are studied via simulation. As illustration, an application to an offence data set is given to show the effectiveness of the proposed model.