• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.3
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• pp.191-214
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• 2014

The goal of this study is an exploratory statistical analysis of the university and college library statistics in the Academic Information Statistics System(rinfo.kr) governed of Korean Education and Research Information Service(KERIS) with performance measures based on sustainability. For the goal, we adopt a preprocessing method to develop change-rate variables by considering preceding predictive elements and succeeding performance elements, and to control external factors, such as size and socioeconomic factors. Then we execute a series of factor analyses and multiple linear regression analyses. 13 factors are extracted by the factor analyses and some sets of significant variables affecting the performance measures are identified through the regression analyses. Based on the results, we discuss the problem of out-lier and low correlation between variables. A suggestion for developing new variables is also discussed based on low effect sizes of the developed regression models. We hope that this study contributes to diffuse discussions on statistics system, evaluation, and further library management based on sustainability.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.43-56
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• 2005

In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.