• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.133-144
• /
• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

• Journal of Preventive Medicine and Public Health
• /
• v.46 no.sup1
• /
• pp.35-42
• /
• 2013

A commitment to physical activity is necessary for personal health, and is a primary goal of physical activity practitioners. Effective practitioners rely on theory and research as a guide to best practices. Thus, sound theory, which is both practical and parsimonious, is a key to effective practice. The purpose of this paper is to review the literature in search of such a theory - one that applies to and explains commitment to physical activity in the form of sport and exercise for youths and adults. The Sport Commitment Model has been commonly used to study commitment to sport and has more recently been applied to the exercise context. In this paper, research using the Sport Commitment Model is reviewed relative to its utility in both the sport and exercise contexts. Through this process, the relevance of the Investment Model for study of physical activity commitment emerged, and a more parsimonious framework for studying of commitment to physical activity is suggested. Lastly, links between the models of commitment and individuals' participation motives in physical activity are suggested and practical implications forwarded.

• ETRI Journal
• /
• v.43 no.1
• /
• pp.74-81
• /
• 2021

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

• Journal of Korean Society on Water Environment
• /
• v.36 no.3
• /
• pp.229-244
• /
• 2020

Hydrologic models, as a useful tool for understanding the hydrologic phenomena in the watershed, have become more complex with the increase of computer performance. The hydrologic model, with complex configurations and powerful performance, facilitates a broader understanding of the effects of climate and soil in hydrologic partitioning. However, the more complex the model is, the more effort and time is required to drive the model, and the more parameters it uses, the less accessible to the user and less applicable to the ungauged watershed. Rather, a parsimonious hydrologic model may be effective in hydrologic modeling of the ungauged watershed. Thus, a semi-distributed hydrologic partitioning model was developed with minimal composition and number of parameters to improve applicability. In this study, the validity and performance of the proposed model were confirmed by applying it to the Namgang Dam, Andong Dam, Hapcheon Dam, and Milyang Dam watersheds among the Nakdong River watersheds. From the results of the application, it was confirmed that despite the simple model structure, the hydrologic partitioning process of the watershed can be modeled relatively well through three vertical layers comprising the surface layer, the soil layer, and the aquifer. Additionally, discussions were conducted on antecedent soil moisture conditions widely applied to stormwater estimation using the soil moisture data simulated by the proposed model.

• Journal of Information Technology Applications and Management
• /
• v.26 no.2
• /
• pp.111-123
• /
• 2019

In this paper, software activity, software activity instance, and the quality of the activity instance are defined as the 'activity which is performed on the software product by a person or a group of persons,' the 'distinctive and individual performance of software activity,' and the 'performer's evaluation on how good or bad his/her own activity instance is,' respectively. The representative values of the instance quality population associated with a product and its sub-population are defined as the (software) activity quality and activity quality characteristic of the product, respectively. The activity quality model in this paper classifies activity quality characteristics according to the classification hierarchy of software activity by the goal. In the model, a quality characteristic can have two types of sub-characteristics : Special sub-characteristic and component sub-characteristic, where the former is its super-characteristic too simultaneously and the latter is not its super-characteristic but a part of its super-characteristic. The activity quality model is parsimonious, coherent, and easy to understand and use. The activity quality model can serve as a corner stone on which a software quality body of knowledge, which constituted with a set of models parsimonious, coherent, and easy to understand and use and the theories explaining the cause-and-relationships among the models, can be built. The body of knowledge can be called the (grand) activity-centric quality model of software.

• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.10
• /
• pp.1382-1387
• /
• 2017

Objective: During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305-day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities. Methods: Data including 60,279 total 305-day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes. Results: The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect. Conclusion: A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

• The KIPS Transactions:PartD
• /
• v.8D no.2
• /
• pp.154-165
• /
• 2001

A number of attempts to develop methods for measuring software effort have been focused on the area of software engineering and many models have also been suggested to estimate the effort of software projects. Almost all current models use algorithmic or statistical mechanisms, but the existing algorithmic effort estimation models have failed to produce accurate estimates. Furthermore, they are unable to reflect the rapidly changing technical environment of software development such as module reuse, 4GL, CASE tool, etc. In addition, these models do not consider the paradigm shift of software engineering and information systems(i.e., Object Oriented system, Client-Server architecture, Internet/Intranet based system etc.). Thus, a new approach to software effort estimation is needed. After reviewing and analyzing the problems of the current estimation models, we have developed a model and a system architecture that will improve estimation performance. In this paper, we have adopted a neural network model to overcome some drawbacks and to increase estimation performance. We will also address the efficient system architecture and estimation procedure by a similar case-based approach and finally suggest the heuristic search method to find the best estimate of target project through empirical experiments. According to our experiment with the optimally parsimonious neural network model the mean error rate was significantly reduced to 14.3%.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.575-587
• /
• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Journal of the Korean Society of Food Culture
• /
• v.23 no.1
• /
• pp.48-54
• /
• 2008

The purpose of this study was to measure the factors influencing customers’ value perception of foodservice in tourist area’s restaurant. A total of 273 questionnaires were completed. Structural equation model was used to measure the causal effects. Results of the study demonstrated that the confirmatory factor analysis model also indicated excellent model fit. The proposed model yielded a significantly better fit to the data than the parsimonious model and extended model. In proposed model, the effects of perceived sacrifice, overall service quality and customer satisfaction on perceived value were statistically significant. The effects of perceived value on loyalty intention were statistically significant. As expected, the overall service quality had a significant effect on customer satisfaction. Moreover, the customer satisfaction played a mediating role in the relationship between overall service quality and loyalty intention.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06a
• /
• pp.49-51
• /
• 2006

분자 wDNF기계를 통해 해 집단을 병렬적으로 탐색하여 유망한 텀들을 선택한 후 그를 구성하는 변수들의 분포를 평가, 확률 모델을 확립하고 그로부터 다음 세대의 해 집단을 구성함으로써 진화 알고리즘의 확장인 EDA을 DNA컴퓨팅으로 모델링한다. 또한 희박한(sparse) 해 집단에서 간략한 (parsimonious) wDNF모델을 항께 찾으므로 단순히 해 집단의 분포만을 진화시켜 나가는 것이 아니라 모델의 구조도 같이 최적화 시켜 나가는 방안을 제시한다.