• Title/Summary/Keyword: Robust 회귀분석

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Technology Innovation Activity and Default Risk (기술혁신활동이 부도위험에 미치는 영향 : 한국 유가증권시장 및 코스닥시장 상장기업을 중심으로)

  • Kim, Jin-Su
    • Journal of Technology Innovation
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    • v.17 no.2
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    • pp.55-80
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    • 2009
  • Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

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The Effects of Socioeconomic Deprivation on Public Library Book Circulation: A Community-level Study (지역사회 사회경제적 박탈이 공공도서관 대출 책수에 미치는 영향)

  • Lee, Jongwook;Kang, Woojin;Lee, Myeong
    • Journal of the Korean Society for Library and Information Science
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    • v.55 no.4
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    • pp.219-243
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    • 2021
  • This study analyzes the effects of community-level socioeconomic deprivations (SED) on public libraries' book circulation in the Seoul metropolitan area. The study design draws upon the theory of local information landscapes, which explains the relationship between community characteristics and information behavior. Using four-year (2015-2018) open government and public library circulation data, we constructed a socioeconomic deprivation index by adjusting a multi-dimensional deprivation index and generated other variables. Multi-level robust linear regression models were used to examine the relationship between SED and public library circulation. In addition, we tested the moderating effects of the library collection size and the number of libraries per unit area, respectively, on library circulation. The results show that there is a significant negative relationship between socioeconomic deprivation and library circulation rate. Also, we found that the size of the library collection negatively moderates the effects of SED in areas with a large number of books, and the number of libraries per unit area was positvely related to the library book circulation, not moderating the effects of SED. These findings suggest that public libraries and policymakers should consider community characteristics in designing strategic plans for public libraries.

Participation Level in Online Knowledge Sharing: Behavioral Approach on Wikipedia (온라인 지식공유의 참여정도: 위키피디아에 대한 행태적 접근)

  • Park, Hyun Jung;Lee, Hong Joo;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.19 no.4
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    • pp.97-121
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    • 2013
  • With the growing importance of knowledge for sustainable competitive advantages and innovation in a volatile environment, many researches on knowledge sharing have been conducted. However, previous researches have mostly relied on the questionnaire survey which has inherent perceptive errors of respondents. The current research has drawn the relationship among primary participant behaviors towards the participation level in knowledge sharing, basically from online user behaviors on Wikipedia, a representative community for online knowledge collaboration. Without users' participation in knowledge sharing, knowledge collaboration for creating knowledge cannot be successful. By the way, the editing patterns of Wikipedia users are diverse, resulting in different revisiting periods for the same number of edits, and thus varying results of shared knowledge. Therefore, we illuminated the participation level of knowledge sharing from two different angles of number of edits and revisiting period. The behavioral dimensions affecting the level of participation in knowledge sharing includes the article talk for public discussion and user talk for private messaging, and community registration, which are observable on Wiki platform. Public discussion is being progressed on article talk pages arranged for exchanging ideas about each article topic. An article talk page is often divided into several sections which mainly address specific type of issues raised during the article development procedure. From the diverse opinions about the relatively trivial things such as what text, link, or images should be added or removed and how they should be restructured to the profound professional insights are shared, negotiated, and improved over the course of discussion. Wikipedia also provides personal user talk pages as a private messaging tool. On these pages, diverse personal messages such as casual greetings, stories about activities on Wikipedia, and ordinary affairs of life are exchanged. If anyone wants to communicate with another person, he or she visits the person's user talk page and leaves a message. Wikipedia articles are assessed according to seven quality grades, of which the featured article level is the highest. The dataset includes participants' behavioral data related with 2,978 articles, which have reached the featured article level, with editing histories of articles, their article talk histories, and user talk histories extracted from user talk pages for each article. The time period for analysis is from the initiation of articles until their promotion to the featured article level. The number of edits represents the total number of participation in the editing of an article, and the revisiting period is the time difference between the first and last edits. At first, the participation levels of each user category classified according to behavioral dimensions have been analyzed and compared. And then, robust regressions have been conducted on the relationships among independent variables reflecting the degree of behavioral characteristics and the dependent variable representing the participation level. Especially, through adopting a motivational theory adequate for online environment in setting up research hypotheses, this work suggests a theoretical framework for the participation level of online knowledge sharing. Consequently, this work reached the following practical behavioral results besides some theoretical implications. First, both public discussion and private messaging positively affect the participation level in knowledge sharing. Second, public discussion exerts greater influence than private messaging on the participation level. Third, a synergy effect of public discussion and private messaging on the number of edits was found, whereas a pretty weak negative interaction effect of them on the revisiting period was observed. Fourth, community registration has a significant impact on the revisiting period, whereas being insignificant on the number of edits. Fifth, when it comes to the relation generated from private messaging, the frequency or depth of relation is shown to be more critical than the scope of relation for the participation level.

Construction of Basin Scale Climate Change Scenarios by the Transfer Function and Stochastic Weather Generation Models (전이함수모형과 일기 발생모형을 이용한 유역규모 기후변화시나리오의 작성)

  • Kim, Byung-Sik;Seoh, Byung-Ha;Kim, Nam-Won
    • Journal of Korea Water Resources Association
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    • v.36 no.3 s.134
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    • pp.345-363
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    • 2003
  • From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.

A comparison of imputation methods using nonlinear models (비선형 모델을 이용한 결측 대체 방법 비교)

  • Kim, Hyein;Song, Juwon
    • The Korean Journal of Applied Statistics
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    • v.32 no.4
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    • pp.543-559
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    • 2019
  • Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.