• Title/Summary/Keyword: 강건모형

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The effect of temperature on the electricity demand: An empirical investigation (기온이 전력수요에 미치는 영향 분석)

  • Kim, Hye-min;Kim, In-gyum;Park, Ki-Jun;Yoo, Seung-Hoon
    • Journal of Energy Engineering
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    • v.24 no.2
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    • pp.167-173
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    • 2015
  • This paper attempts to estimate the electricity demand function in Korea with quarterly data of average temperature, GDP and electricity price over the period 2005-2013. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the electricity demand function. The results show that short-run price and income elasticities of the electricity demand are estimated to be -0.569 and 0.631, respectively. They are statistically significant at the 1% level. Moreover, long-run income and price elasticities are estimated to be 1.589 and -1.433, respectively Both of results reveal that the demand for electricity is price- and income-elastic in the long-run. The relationship between electricity consumption and temperature is supported by many of references as a U-shaped relationship, and the base temperature of electricity demand is about $15.2^{\circ}C$. It is shown that power of explanation and goodness-of-fit statistics are improved in the use of the lagged dependent variable model rather than conventional model.

Effects of Knowledge Management Activities on Financial Performance: Focusing on the Mediating Effects of Enterprise-level Resilience (기업의 지식경영활동이 재무적 성과에 미치는 영향: 기업 회복역량의 매개효과를 중심으로)

  • Kang, Yeonsil;Cho, Booyun
    • Knowledge Management Research
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    • v.22 no.1
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    • pp.139-154
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    • 2021
  • As environmental impacts on business management rapidly rise, the importance of resilience has been emphasized ever. Resilience can be defined as the capability to quickly return to daily operational levels after external shocks. Thus, we suggest and empirically verify a conceptual model using enterprise-level resilience in knowledge management. Based on previous studies, sub-components of resilience have been distinguished into R4(i.e., Robustness, Redundancy, Resourcefulness, and Rapidity) and the internal and external knowledge management activities of companies have been identified as prior factors. Considering resilience as mediating variables, firms' knowledge management activities are assumed to affect a firms' performance. We collected data with the structured survey which had been developed from the stepwise approach to verify the structural model that leads to knowledge management activities, resilience, and performance. In further research, we request exploratory research on independent variables that can increase the firms' resilience and on dependent variables that would be influenced by the enterprise-level resilience.

On the use of weighted adaptive nearest neighbors for missing value imputation (가중 적응 최근접 이웃을 이용한 결측치 대치)

  • Yum, Yunjin;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.31 no.4
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    • pp.507-516
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    • 2018
  • Widely used among the various single imputation methods is k-nearest neighbors (KNN) imputation due to its robustness even when a parametric model such as multivariate normality is not satisfied. We propose a weighted adaptive nearest neighbors imputation method that combines the adaptive nearest neighbors imputation method that accounts for the local features of the data in the KNN imputation method and weighted k-nearest neighbors method that are less sensitive to extreme value or outlier among k-nearest neighbors. We conducted a Monte Carlo simulation study to compare the performance of the proposed imputation method with previous imputation methods.

Bootstrapping Composite Quantile Regression (복합 분위수 회귀에 대한 붓스트랩 방법의 응용)

  • Seo, Kang-Min;Bang, Sung-Wan;Jhun, Myoung-Shic
    • The Korean Journal of Applied Statistics
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    • v.25 no.2
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    • pp.341-350
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    • 2012
  • Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

Temporal hierarchical forecasting with an application to traffic accident counts (시간적 계층을 이용한 교통사고 발생건수 예측)

  • Jun, Gwanyoung;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.31 no.2
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    • pp.229-239
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    • 2018
  • This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

A literature review on RSM-based robust parameter design (RPD): Experimental design, estimation modeling, and optimization methods (반응표면법기반 강건파라미터설계에 대한 문헌연구: 실험설계, 추정 모형, 최적화 방법)

  • Le, Tuan-Ho;Shin, Sangmun
    • Journal of Korean Society for Quality Management
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    • v.46 no.1
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    • pp.39-74
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    • 2018
  • Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

An Optimal Process Design U sing a Robust Desirability Function(RDF) Model to Improve a Process/Product Quality on a Pharmaceutical Manufacturing Process (제약공정에서 공정 및 제품의 품질향상을 위해 강건 호감도 함수 모형을 이용한 최적공정설계)

  • Park, Kyung-Jin;Shin, Sang-Mun;Jeong, Hea-Jin
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.33 no.1
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    • pp.1-9
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    • 2010
  • Quality design methodologies have received constituent attention from a number of researchers and practitioners for more than twenty years. Specially, the quality design for drug products must be carefully considered because of the hazards involved in the pharmaceutical industry. Conventional pharmaceutical formulation design problems with mixture experiments have been typically studied under the assumption of an unconstrained experimental region with a single quality characteristic. However, real-world pharmaceutical industrial situations have many physical limitations. We are often faced with multiple quality characteristics with constrained experimental regions. ln order to address these issues, the main objective of this paper is to propose a robust desirability function (RDF) model using a desirability function (DF) and mean square error (MSE) to simultaneously consider a number of multiple quality characteristics. This paper then present L-pseudocomponents and U-pseudocomponents to handle physical constraints. Finally, a numerical example shows that the proposed RDF can efficiently be applied to a pharmaceutical process design.

Loss-adjusted Regularization based on Prediction for Improving Robustness in Less Reliable FAQ Datasets (신뢰성이 부족한 FAQ 데이터셋에서의 강건성 개선을 위한 모델의 예측 강도 기반 손실 조정 정규화)

  • Park, Yewon;Yang, Dongil;Kim, Soofeel;Lee, Kangwook
    • Annual Conference on Human and Language Technology
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    • 2019.10a
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    • pp.18-22
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    • 2019
  • FAQ 분류는 자주 묻는 질문을 범주화하고 사용자 질의에 대해 가장 유사한 클래스를 추론하는 방식으로 진행된다. FAQ 데이터셋은 클래스가 다수 존재하기 때문에 클래스 간 포함 및 연관 관계가 존재하고 특정 데이터가 서로 다른 클래스에 동시에 속할 수 있다는 특징이 있다. 그러나 최근 FAQ 분류는 다중 클래스 분류 방법론을 적용하는 데 그쳤고 FAQ 데이터셋의 특징을 모델에 반영하는 연구는 미미했다. 현 분류 방법론은 이러한 FAQ 데이터셋의 특징을 고려하지 못하기 때문에 정답으로 해석될 수 있는 예측도 오답으로 여기는 경우가 발생한다. 본 논문에서는 신뢰성이 부족한 FAQ 데이터셋에서도 분류를 잘 하기 위해 손실 함수를 조정하는 정규화 기법을 소개한다. 이 정규화 기법은 클래스 간 포함 및 연관 관계를 반영할 수 있도록 오답을 예측한 경우에도 예측 강도에 비례하여 손실을 줄인다. 이는 오답을 높은 확률로 예측할수록 데이터의 신뢰성이 낮을 가능성이 크다고 판단하여 학습을 강하게 하지 않게 하기 위함이다. 실험을 위해서는 다중 클래스 분류에서 가장 좋은 성능을 보이고 있는 모형인 BERT를 이용했으며, 비교 실험을 위한 정규화 방법으로는 통상적으로 사용되는 라벨 스무딩을 채택했다. 실험 결과, 본 연구에서 제안한 방법은 기존 방법보다 성능이 개선되고 보다 안정적으로 학습이 된다는 것을 확인했으며, 데이터의 신뢰성이 부족한 상황에서 효과적으로 분류를 수행함을 알 수 있었다.

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Estimation of Genetic Parameter for Linear Type Traits in Holstein Dairy Cattle in Korea (Holstein종 젖소의 선형심사형질에 대한 유전모수추정)

  • Lee, Ki-Hwan;Sang, Byung-Chan;Nam, Myoung-Soo;Do, Chang-Hee;Choi, Jae-Gwan;Cho, Kawng-Hyun
    • Journal of Animal Science and Technology
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    • v.51 no.5
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    • pp.345-352
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    • 2009
  • This study utilized 332,625 records of linear type scores consisting for 15 primary traits, 22,175 final score and 84,612 pedigree information of 22,175 Holstein cows from 1993 to 2007 in Korea to estimate genetic parameters for 16 type traits. Genetic and error (co)variances between two traits selected from 16 traits were estimated using bi-trait pairwise analyses with DFREML package. The estimated heritabilities for stature (ST), strength (STR), body depth (BD), dairy form (DF), rump angle (RA), thurl width (TW), rear legs side view (RLSV), foot angle (FA), fore udder attachment (FUA), rear udder height (RUH), rear udder width (RUW), udder cleft (UC), udder depth (UD), front teat placement (FTP), front teat length (FTL) and final score (FS) were 0.31, 0.21, 0.25, 0.10, 0.29, 0.19, 0.09, 0.06, 0.12, 0.13, 0.12, 0.08, 0.26, 0.20, 0.28 and 0.15, respectively. ST had the highest positive genetic correlation with BD (0.90), while RLSV had the highest negative genetic correlation with FA (-0.56). RA had negative genetic correlation with most udder traits (-0.17~-0.02). Especially, RUW had the higher positive genetic correlation with STR (0.60), BD (0.62), and TW (0.49), however, UD had the higher negative genetic correlation with STR (-0.40) and BD (-0.40). FTL had negative genetic correlation with FUA, RUH, RUW, UC and UD. FS had positive genetic correlation with UC, UD and FTP (0.12, 0.18 and 0.20). However, additional research is needed on the use of these parameters in the genetic evaluation because estimated genetic and error variance-covariance matrices were not positive definite.

An Analysis of the Determinants of Employment Productivity in Korean Transportation Industry Using Korea Labor and Income Panel Study (한국노동패널자료를 활용한 국내 운송업 고용생산성 결정요인 분석)

  • So, Ae-rim;Shin, Seung-sik
    • Journal of Korea Port Economic Association
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    • v.35 no.1
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    • pp.57-76
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    • 2019
  • This study deals with the determinants of employment productivity of transportation labor, who are the main agents of the transportation industry that has made significant contributions to our country's industrial development. The study selected the determinants of employment productivity using the Korea Labor and Income Panel Study data, and analyzed the effects of various factors using panel logistic regression, panel OLS model, and panel robust regression. The results were as follows. First, a more positive effect was shown when employees held a regular job, had a "high level of education", "joining the labor union" and "experiencing vocational training". Second, in the case of job security, having a "high level of education" and "joining the labor union" showed a more positive effect; further, job security was higher for employees who worked in a "big company" or were "married". Third, in the case of higher income productivity, higher values of "age", "academic ability" and "company size" had a more positive effect, whereas larger values of "education" and "health condition except job training" had a negative one. Fourth, in the case of job satisfaction, "female", "joining the labor union" and having a higher "income" or "job security" led to higher satisfaction and a better "health condition compared to an average person". Further, a higher "overall life satisfaction" and "economic level" led to lower job satisfaction. The analysis of the determinants of employment productivity of transportation business and seeking for improvement plan is expected to improve the employment productivity in the transportation business.