• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.583-592
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• 2015

Zero-inflation has recently attracted much attention in integer-valued time series. This article deals with conditional variance (volatility) modeling for the zero-inflated count time series. We incorporate zero-inflation property into integer-valued GARCH (INGARCH) via conditional Poisson and negative binomial marginals. The Cholera frequency time series is analyzed as a data application. Estimation is carried out using EM-algorithm as suggested by Zhu (2012).

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.37-46
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• 2020

Recurrent event data frequently occur in clinical studies, demography, engineering reliability and so on (Cook and Lawless, The Statistical Analysis of Recurrent Events, Springer, 2007). Sometimes, two or more different but related type of recurrent events may occur simultaneously. In this study, our interest is to estimate the covariate effect on bivariate recurrent event times with zero inflations. Such zero inflation can be related with susceptibility. In the context of bivariate recurrent event data, furthermore, such susceptibilities may be different according to the type of event. We propose a joint model including both two intensity functions and two cure rate functions. Bivariate frailty effects are adopted to model the correlation between recurrent events. Parameter estimates are obtained by maximizing the likelihood derived under a piecewise constant hazard assumption. According to simulation results, the proposed method brings unbiased estimates while the model ignoring cure rate models gives underestimated covariate effects and overestimated variance estimates. We apply the proposed method to a set of bivariate recurrent infection data in a study of child patients with leukemia.

• KDI Journal of Economic Policy
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• v.43 no.2
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• pp.81-101
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• 2021

This paper estimates the expectation-augmented Phillips curve, which explains inflation dynamics, in Korea. The phenomenon of low inflation in Korea has been going on for quite some time, in particular since 2012. During the Covid-19 crisis, due to low inflation expectations the operation of monetary policy was limited as the base rate approached the zero lower bound. The main objective of this paper is to estimate where and how tightly inflation expectations are anchored. It was found that long-term inflation expectations fell to around 1%, falling short of the inflation target, and that inflation expectations are strongly anchored to long-term expectations, which implies that the low inflation phenomenon is likely to extend into the future. The results also imply that even if inflation fluctuates due to temporary disturbances, it may converge to a level below the inflation target. The slight rebound of long-term expectations during the Covid-19 crisis suggests that the aggressive monetary policy may have contributed to improving economic agents' beliefs about the commitment of monetary authorities to inflation stability. This may also help long-term expectations gradually to approach the inflation target.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.115-122
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• 2015

This article is concerned with count time series taking values in non-negative integers. Along with the first order mean of the count time series, conditional variance (volatility) has recently been paid attention to and therefore various integer-valued GARCH(generalized autoregressive conditional heteroscedasticity) models have been suggested in the last decade. We introduce diverse integer-valued GARCH(INGARCH, for short) processes to count time series and a real data application is illustrated as a case study. In addition, zero inflated INGARCH models are discussed to accommodate zero-inflated count time series.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.895-900
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• 2013

In this research, we propose a simple bivariate zero inflated negative binomial regression model with different dispersion for bivariate count data with excess zeros. An application to the demand for health services shows that the proposed model is better than existing models in terms of log-likelihood and AIC.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.859-864
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• 2012

Poisson regression and negative binomial regression are usually used to analyze counting data; however, these models are unsuitable for fit zero-inflated data that contain unexpected zero-valued observations. In this paper, we review the zero-inflated regression in which Bernoulli process and the counting process are hierarchically mixed. It is known that zero-inflated regression can efficiently model the over-dispersion problem. Vuong statistic is employed to compare performances of the zero-inflated models with other standard models.

• Journal of Wellbeing Management and Applied Psychology
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• v.3 no.1
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• pp.21-31
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• 2020

As people's income rises dramatically, people's happiness seems not as high as expected. In fact, there are two different arguments about the relationship between income level and happiness. The focus of the debate is whether the correlation between income and probability of happiness is positive or negative. Therefore, we hypothesizes that the relationship between income and probability of happiness presents an inverted U-shaped curve. Then, this paper sets China as an example to explore the effect of income on happiness. The data from the Chinese General Social Survey (CGSS) in 2015 is employed to conduct empirical analyses under the Probit model and the Zero-Inflation-Passion model. The empirical findings indicate that the effect of income on happiness presents an inverted U-shaped curve and significantly in statistic. Meanwhile, spouse's income, educational level, marriage time and house property have a positive and significant effect on happiness. Conversely, age and local living standards have a negative and significant effect on happiness. Unfortunately, even though registered residence and children have a negative effect on happiness, they do not get through the significant test. In order to ensure the robustness of our empirical results, we test the robustness of the above empirical results by adjusting the sample size. The results of robustness test verify that our empirical results are robust. Moreover, this paper also makes a small contribution to the current literature with a sample from China.

• Journal of Biosystems Engineering
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• v.27 no.6
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• pp.501-512
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• 2002

Traction performance of off-road vehicles is estimated using slip-traction relationships Two zero condition accepted by ASAE have been used widely to obtain the slip-traction relationships of off-road vehicles. This study was carried out using the soil bin systems to investigate the characteristic of slip-traction curves obtained using two zero conditions defined by ASAE. which are driving and driven zero condition, and to present disadvantage of slip-traction relationship based on two zero conditions of ASAE. The results of this study are summarized as follows : 1. For the driving zero condition, the curve of slip-traction relationship shows some issues. The first question is that the slip is zero when the traction is zero. The second question is that the value of slip is smaller than that of corresponding real slip, as the rolling radius decreased f3r the setting zero condition with driving wheel. 2. For the driven zero condition. slip occurs when the traction is zero, which is more realistic results than driving zero condition. But when a zero condition is set, skid occurs and this result increased the rolling radius of tire and increased slip value f3r the specific traction value of whole slip range. This kind of trend was getting bigger as the soil is softer, or the tire inflation pressure is higher. 3. From the results of this study, it was found that slip-traction relationship obtained by two zero conditions of ASAE is not realistic in estimating the traction performance of off-road vehicles. And also slip-traction relationship obtained for the same experimental condition showed different result in accordance with chosen zero condition,

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.571-579
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• 2011

We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

• International Journal of Highway Engineering
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• v.18 no.4
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• pp.83-92
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• 2016

PURPOSES : The purpose of this study was to develop safety performance functions (SPFs) that use zero-inflated negative binomial regression models for urban intersections in central business districts (CBDs), and to compare the statistical significance of developed models against that of regular negative binomial regression models. METHODS : To develop and analyze the SPFs of intersections in CBDs, data acquisition was conducted for dependent and independent variables in areas of study. We analyzed the SPFs using zero-inflated negative binomial regression model as well as regular negative binomial regression model. We then compared the results by analyzing the statistical significance of the models. RESULTS : SPFs were estimated for all accidents and injury accidents at intersections in CBDs in terms of variables such as AADT, Number of Lanes at Major Roads, Median Barriers, Right Turn with an Exclusive Turn Lane, Turning Guideline, and Front Signal. We also estimated the log-likelihood at convergence and the likelihood ratio of SPFs for comparing the zero-inflated model with the regular model. In he SPFs, estimated log-likelihood at convergence and the likelihood ratio of the zero-inflated model were at -836.736, 0.193 and -836.415, 0.195. Also estimated the log-likelihood at convergence and likelihood ratio of the regular model were at -843.547, 0.187 and -842.631, 0.189, respectively. These figures demonstrate that zero-inflated negative binomial regression models can better explain traffic accidents at intersections in CBDs. CONCLUSIONS : SPFs that use a zero-inflated negative binomial regression model demonstrate better statistical significance compared with those that use a regular negative binomial regression model.