• Title/Summary/Keyword: multifactor

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MULTIFACTOR MODELLING IN CONSTRUCTION MANAGEMENT

  • Leszek Janusz;Oleg Kaplinski
    • International conference on construction engineering and project management
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    • 2005.10a
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    • pp.633-637
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    • 2005
  • The paper presents a multifactor modelling of construction processes. There are three phases of the proposed extended procedure. Tools for these phases from chronometric test to verifying of the assumed model are indicated. Apart from the classic verification activities the method of artificial neural networks has been successfully applied. The paper presents the usage of these tools to model the process of assembly of structural corrugated steel plate structures.

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Multifactor-Dimensionality Reduction in the Presence of Missing Observations

  • Chung, Yu-Jin;Lee, Seung-Yeoun;Park, Tae-Sung
    • Proceedings of the Korean Statistical Society Conference
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    • 2005.11a
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    • pp.31-36
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    • 2005
  • An identification and characterization of susceptibility genes for common complex multifactorial diseases is a challengeable task, in which the effect of single genetic variation will be likely dependent on other genetic variations(gene-gene interaction) and environmental factors (gene-environment interaction). To address is issue, the multifactor dimensionality reduction (MDR) has been proposed and implemented by Ritchie et al. (2001), Moore et al. (2002), Hahn et al.(2003) and Ritchie et al. (2003). With MDR, multilocus genotypes effectively reduce the dimension of genotype predictors from n to one, which improves the identification of polymorphism combinations associated with disease risk. However, MDR cannot handle missing observations appropriately, in which missing observation is treated as an additional genotype category. This approach may suffer from a sparseness problem since when high-order interactions are considered, an additional missing category would make the contingency table cells more sparse. We propose a new MDR approach with minimum loss of sample sizes by considering missing data over all possible multifactor classes. We evaluate the proposed MDR by using the prediction errors and cross validation consistency.

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Main SNP Identification of Hanwoo Carcass Weight with Multifactor Dimensionality Reduction(MDR) Method (MULTIFACTOR DIMENSIONALITY REDUCTION(MDR)을 이용한 한우 도체중에서의 주요 SNP 규명)

  • Lee, Jea-Young;Kim, Dong-Chul
    • The Korean Journal of Applied Statistics
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    • v.21 no.1
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    • pp.53-63
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    • 2008
  • It is commonly believed that disease of human or economic traits of livestock are caused not by single gene acting alone, but by multiple genes interacting with one an-other. This issue is difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction (MDR) nonparametric statistical method, to improve the identification of single nucleotide polymorphism (SNP) associated with the Hanwoo(Korean cattle) carcass cold weight, is applied and compared with ANOVA results.

Can Bank Credit for Household be a Conditional Variable for Consumption CAPM? (가계대출을 조건변수로 사용하는 소비 준거 자본자산 가격결정모형)

  • Kwon, Ji-Ho
    • Asia-Pacific Journal of Business
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    • v.11 no.3
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    • pp.199-215
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    • 2020
  • Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

  • Hong, Yingfu;Lee, Sangbum;Oh, Sejong
    • ETRI Journal
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    • v.38 no.1
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    • pp.206-215
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    • 2016
  • The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

Behavioral Analysis Zero-Trust Architecture Relying on Adaptive Multifactor and Threat Determination

  • Chit-Jie Chew;Po-Yao Wang;Jung-San Lee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.9
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    • pp.2529-2549
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    • 2023
  • For effectively lowering down the risk of cyber threating, the zero-trust architecture (ZTA) has been gradually deployed to the fields of smart city, Internet of Things, and cloud computing. The main concept of ZTA is to maintain a distrustful attitude towards all devices, identities, and communication requests, which only offering the minimum access and validity. Unfortunately, adopting the most secure and complex multifactor authentication has brought enterprise and employee a troublesome and unfriendly burden. Thus, authors aim to incorporate machine learning technology to build an employee behavior analysis ZTA. The new framework is characterized by the ability of adjusting the difficulty of identity verification through the user behavioral patterns and the risk degree of the resource. In particular, three key factors, including one-time password, face feature, and authorization code, have been applied to design the adaptive multifactor continuous authentication system. Simulations have demonstrated that the new work can eliminate the necessity of maintaining a heavy authentication and ensure an employee-friendly experience.

Gene-gene interaction of CCND1, ESR1 and CDK7 on the risk of breast cancer detected by multifactor dimensionality reduction and logistic regression (유방암과 CCND1, ESR1, CDK7 유전자 다형성의 상호작용; 로지스틱 회귀분석과 multifactor dimensionality reduction(MDR)의 분석 비교)

  • Choe Ji-Yeop;Ritchie Marylyn D.;Motsinger Alison A.;Lee Gyeong-Mu;No Dong-Yeong;Yu Geun-Yeong;Moore Jason H.;Gang Dae-Hui
    • 대한예방의학회:학술대회논문집
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    • 2004.10a
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    • pp.40.1-40.1
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    • 2004
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Decomposition of Employment Growth in Korean Metropolitan Labor Markets: An Application of a Four-way Multifactor Partitioning (국내 7대 특·광역시 노동시장의 고용성장 요인분해 - 네 변인 다요인분해분석의 적용 -)

  • Jihan Park;Donghyn Kim
    • Journal of the Korean Regional Science Association
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    • v.39 no.4
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    • pp.53-71
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    • 2023
  • This study aims to identify the contribution of factors to employment growth over the past 20 years (1996-2016) for seven metropolitan areas in Korea. For this purpose, we performed a multifactor partitioning (MFP) analysis based on the business survey data provided by Statistics Korea. The key findings of the analysis are as follows. First, over the long run, the region effect is dominant in metropolitan employment growth, followed by the industry mix effect. On the other hand, the dynamic MFP findings suggests that future regional employment disparities are likely to be explained by industry structure. Second, the gender mix and decent job mix effect do not significantly contribute to regional employment growth. However, the contributions of individual factors are not invalid, and it is possible to infer a pattern of declining employment for men-permanent workers and increasing employment for women-contingent workers. These results indicate the importance and necessity of employment policies that can promote structural transition in regional industries and qualitative growth accompanied by employment stability.

Identification of epistasis in ischemic stroke using multifactor dimensionality reduction and entropy decomposition

  • Park, Jung-Dae;Kim, Youn-Young;Lee, Chae-Young
    • BMB Reports
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    • v.42 no.9
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    • pp.617-622
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    • 2009
  • We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-$\beta{1}$, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes.

Gene-Gene Interaction Analysis for the Accelerated Failure Time Model Using a Unified Model-Based Multifactor Dimensionality Reduction Method

  • Lee, Seungyeoun;Son, Donghee;Yu, Wenbao;Park, Taesung
    • Genomics & Informatics
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    • v.14 no.4
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    • pp.166-172
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    • 2016
  • Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.