This study reviews the SERVQUAL model theoretically and statistically in relation to the nonlinearity of the perceived service quality. Perceived service quality measurement based on the SERVQUAL model assumes that consumers evaluate service quality by comparing their perceptions with their expectations. However, previous studies adopt linear factor analysis to discuss the SERVQUAL model. The present study assumes that consumers evaluate the service quality with a standard to admit the difference between their expectations and perceptions, and that their perceived service quality follows a nonlinear response. A nonlinear SERVQUAL model based on a nonlinear factor analysis model is proposed to understand the characteristics. The proposed model employs a threshold specification that represents the space in which consumers admit their discrepancy. The study extends a nonlinear factor analysis model to a nonparametric model in order to examine the functional aspects that from the perceived service quality. A nonparametric SERVQUAL model is adopted without any assumptions of the functional form. The empirical studies on the retail sector shows that the nonlinear model performs better than the linear model, and that the nonparametric model estimates the nonlinear function for perceived service quality. The results from the proposed model in our study provide insights in a consumer’s perceived service quality recognized as nonlinear and asymmetric. We conclude that the functional form for perceived service quality should be considered when specifying the measurement model for SERVQUAL. In addition, we discuss future work for a nonlinear measurement model and a nonparametric factor analysis.

This presentation introduces a methodological framework that analyzes a model of destination image formation (Baloglu & McCleary 1999; Beerli & Martin 2004). Specifically, the main aims of this study are to investigate what type of stimulus factors (information sources) are connected to the formation of destination image, and to explore if there is a connection between their strength of willingness to visit a destination and their patterns to associate with the destination. The study employs an advanced nonparametric Bayesian relational model (Glückstad, Herlau, Schmidt, Rzepka, Araki and Mørup 2013; Mørup, Glückstad, Herlau & Schmidt, 2014) for a two-steps analysis . The first step attempts to segment consumers according to patterns of attributes consumers associate with three arbitrary selected destinations. The second step statistically analyzes latent structural patterns per segment by contrasting two independent datasets, one consisting of information sources and members of a segment and another consisting of destination attributes and the members of the segment. The results of two-steps analysis demonstrated that patterns of attributes respondents associate with the three selected destinations differ across individuals and the applied method enabled to segment respondents according to the differences, and consumers’ associations, their willingness to visit the destinations and types of information sources they have accessed to learn about the destinations are connected to each other.

신뢰성 해석 및 신뢰성기반 최적설계는 불확실성을 고려한 확률변수를 입력 값으로 요구하며, 확률변수는 모수적․비모수 적 통계모델링 방법을 사용하여 확률분포함수의 형태로 정량화 된다. 신뢰성 해석과 같은 통계적 해석은 입력되는 확률분포 함수의 특성이 결과값에 영향을 미치게 되며, 확률분포함수는 통계모델링 방법에 따라 다른 형태를 가지게 된다. 본 연구에 서는 모수적 통계모델링 방법인 순차적 통계모델링 방법과 비모수적 방법인 커널밀도추정을 사용하여 데이터의 개수에 따 른 통계모델링의 결과를 분석하였다. 또한 수치예제를 통해 두 가지 기법에 따른 신뢰성 해석의 결과를 분석하였고, 데이터 의 개수에 따른 적절한 기법을 제안하였다.

Based on latest research, the parametric quality statistics cannot be used in non-normal process with demand pattern of many-variety and small-volume, since it involves extremely small sample size. The research proposes nonparametric quality statistics according to the number of lot or batch in the non-normal process. Additionally, the nonparametric Process Capability Index (PCI) is used with 14 identified non-normal distributions.

The present study proposes two normality tests using nonparametric rank measures for small sample such as modified normal probability paper(NPP) tests and modified Ryan-Joiner Test. This research also reviews various normality tests such as 2 test, and Kullback-Leibler test. The proposed normality tests can be efficiently applied to the sparsity tests of factor effect or contrast using saturated design in kn factorial and fractional factorial design.

This paper reviews nonparametric statistics by Neyman-Pearson test and Fisher test. Nonparametric statistics deal with the small sample with distribution-free assumption in multi-product and small-volume production. Two tests for various nonparametric statistic methods such as sign test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Mood test, Friedman test and run test are also presented with the steps for testing hypotheses and test of significance.

In this study, seasonal Mann - Kendall test method was applied to 12 stations of the water quality measurement network of Nam-River based on data of BOD, COD, TN and TP for 11 years from January 2005 to December 2015 The changes of water quality at each station were examined through linear trends and the tendency of water quality change during the study period was analyzed by applying the locally weighted scatter plot smoother (LOWESS) method. In addition, spatial trends of the whole Nam-River were examined by items. The flow-adjusted seasonal Kendall test was performed to remove the flow at the water quality measurement station. As a result, BOD, COD concentration showed "no trand" and TN and TP concentration showed "down trand" in regional Kendall test throughout the study period. BOD and TP concentration in "no trand", COD, and TN concentration showed an "up trand" tendency in Nam-River dam. LOWESS analysis showed no significant water quality change in most of the analysis items and stations, but water quality fluctuation characteristics were shown at some stations such as NR1 (Kyungho-River 1), NR2 (Kyungho-River 2), NR3 (Nam-River), NR6 (Nam-River 2A). In addition, the flow-adjusted seasonal Kendall results showed that the BOD concentration was "up trand" due to the flow at the NR3 (Nam-River) station. The COD concentration was "up trand" due to the flow at NR1 (Kyungho-River 1) and NR2 (Kyungho-River 2) located upstream of the Nam-River. The effect of influent flow on water quality varies according to each site and analysis item. Therefore, for the effective water quality management in the Nam-River, it is necessary to take measures to improve the water quality at the point where the water quality is continuously "up trand" during the study period.

이 논문은 비모수추정법에 의한 조건부가치측정법을 이용하여 부산시 가정용수 수질개선에 대한 지불의사액을 추정하는 것이다. 이와 같은 연구는 생활용수를 관리하는 정책입안자들에게 수질개선에 따른 경제적 편익의 정보를 제공하고, 부산시의 생활용수 개선사업을 수행여부나 수행규모를 결정하는데 있어서 유용한 정보와 시사점을 제공할 수 있다. 비모수추정법은 모수추정법에서 가정되는 모형분포의 적합도, 모형설정, 이분산 등의 가정과 검정이 필요 없을 뿐만 아니라 비교적

Prediction of rice developmental stage is necessary for proper crop management and a prerequisite for growth simulation as well. The objectives of the present study were to find out the relationship between the plastochrone index(PI) and the developmental index(DVI) estimated by non-parametric phenology model which simulates the duration from seedling emergence(DVI=0) to heading(DVI=l) by employing daily mean air temperature and daylength as predictor variables, and to confirm the correspondency of developmental indice to panicle developmental stages based on this relationship. Four japonica rice cultivars, Kwanakbyeo, Sangpungbyeo, Dongjinbyeo, and Palgumbyeo which range from very early to very late in maturity, were grown by sowing directly in dry paddy field five times at an interval of two weeks. Data for seedling emergence, leaf appearance, differentiation stage of primary rachis branch and heading were collected. The non-parametric phenology model predicted well the duration from seedling emergence to heading with errors of less than three days in all sowings and cultivars. PI was calculated for every leaf appearance and related to the developmental index estimated for corresponding PI. The stepwise polynomial analysis produced highly significant square-rooted cubic or biquadratic equations depending on cultivars, and highly significant square-rooted biquadratic equation for pooled data across cultivars without any considerable reduction in accuracy compared to that for each cultivar. To confirm the applicability of this equation in predicting the panicle developmental stage, DVI at differentiation stage of primary rachis branch primordium was calculated by substituting PI with 82 corresponding to this stage, and the duration reaching this DVI from seedling emergence was estimated. The estimated duration revealed a good agreement with that observed in all sowings and cultivars. The deviations between the estimated and the observed were not greater than three days, and significant difference in accuracy was not found for predicting this developmental stage between those equations derived for each cultivar and for pooled data across all cultivars tested.