- 著者
- Youhei Tomio Hideki Nagatsuka
- 出版者
- The Japanese Society for Quality Control
- 雑誌
- Total Quality Science (ISSN:21893195)
- 巻号頁・発行日
- vol.7, no.3, pp.137-148, 2022-05-11 (Released:2022-05-12)
- 参考文献数
- 19
Conway and Maxwell derived the Conway-Maxwell (COM)-Poisson distribution as generalization of the Poisson distribution. This distribution has been used in survival analysis. The probability mass function (pmf) of this distribution contains a normalizing constant expressed as sum of infinite series and therefore, not only the computation of the distribution but also the parameter estimation for the COM-Poisson is difficult. To remedy this problem, several methods have been appeared in the literature such as the methods based on Laplace approximation and linear regression. However, it is pointed out that the approximation accuracy of the Laplace approximation is poor, and the regression method cannot be applied if there are no covariates.In this paper, we propose a new method of parameter estimation for the COM-Poisson using the conditional likelihood functions in the COM-Poisson distribution. The key idea of the proposed method is to use the conditional likelihood functions, which does not have the complicated normalizing constant. We further prove that the estimates of all two parameters always exist uniquely and a conditional likelihood function of the shape parameter is a log-concave function. Through Monte Carlo simulations, we further show that the proposed method performs better than the existing method in terms of bias and root mean squared error (RMSE). In an illustrative example, we fit the COM-Poisson model to the real data set of carton by our proposed method.
- 著者
- Yuuki Sugiyama Takumi Arai Tianxiang Yang Tairiku Ogihara Masayuki Goto
- 出版者
- The Japanese Society for Quality Control
- 雑誌
- Total Quality Science (ISSN:21893195)
- 巻号頁・発行日
- vol.4, no.3, pp.109-118, 2019-07-31 (Released:2019-08-09)
- 参考文献数
- 15
In recent years, many companies conducting recruitment activities and many students looking for a job use internet portal sites for job-hunting in Japan. Companies can post their basic information on individual company pages and recruit applications from students. On the other hand, student users can gauge corporate attractiveness by browsing individual company pages on a job-hunting site and can make entries to companies of interest. Therefore, a large amount of their behavior history data is accumulated on the site. There are several studies on prediction of users' entries to companies and analysis of preference using user attribute information and entry history data. However, in the conventional researches, browsing activities on individual company pages existing in the background of the user's entry were not considered, so the relation between browsing and making entries has not been studied. This research proposes a latent class model for analyzing the relation between browsing company pages and making entries to companies. The proposed model enables clarification of target users and consideration of effective promotion activities. Through a demonstrative analysis using actual data on a major job-hunting website in Japan, we show the effectiveness of the proposed model.