Factor Modeling(因子建模)研究综述
Factor Modeling 因子建模 - Machine learning provides a more general framework for financial modeling than its linear parametric predecessors, generalizing archetypal modeling approaches, such as factor modeling, derivative pricing, portfolio construction, optimal hedging with model-free, data-driven approaches which are more robust to model risk and capture outliers. [1] This paper focuses on the problem of multi-factor modeling and reasoning about the trusted relationship between elements, and proposes an association probability analysis model based on multi-element fusion. [2] Score and construct concordance were evaluated using equipercentile equating and bifactor modeling, respectively. [3] General and specific domains of social functioning were identified using bi-factor modeling. [4] METHODS Bifactor modeling was used to create the latent internalizing factors in a treatment-seeking sample of emerging adults (n = 356). [5] This paper proposes a new approach to factor modeling based on the long-run equilibrium relation between prices and related drivers of risk (integrated factors). [6] This study, conducted among a sample of 310 child protection workers, assessed the construct validity of this measure using confirmatory factor analysis (CFA) and bifactor modeling. [7] To address this issue, we propose a novel (co-)integrated methodology to factor modeling based on both prices and returns. [8] (2019) with bifactor modeling, evidence regarding its measurement invariance across sex and somatic diseases is still missing. [9] The aim of this paper is to investigate the optimum conditions for biodiesel production by methanolysis of beef tallow over chicken eggshell derived CaO catalyst, via a 3-level 5-factor modeling using response surface methodology (RSM). [10] Results are interpreted as preliminary evidence for the utility of bifactor modeling in understanding the latent structure of self-harm. [11] METHODS Confirmatory factor analyses, bifactor modeling, and structural equation modeling (SEM) were used with data gathered at pretreatment and posttreatment as part of a large randomized clinical trial. [12]机器学习为金融建模提供了比其线性参数前辈更通用的框架，概括了原型建模方法，例如因子建模、衍生品定价、投资组合构建、使用无模型、数据驱动的方法进行最优对冲，这些方法对风险建模更稳健并捕获异常值。 [1] 本文针对元素间可信关系的多因素建模和推理问题，提出了一种基于多元素融合的关联概率分析模型。 [2] 分数和构造一致性分别使用等百分位数和双因子模型进行评估。 [3] 使用双因素模型确定了社会功能的一般和特定领域。 [4] 方法 双因子模型用于在寻求治疗的新兴成年人样本中创建潜在的内化因子（n = 356）。 [5] 本文提出了一种基于价格与相关风险驱动因素（综合因素）之间的长期均衡关系的因子建模新方法。 [6] 这项研究在 310 名儿童保护工作者的样本中进行，使用验证性因素分析 (CFA) 和双因素模型评估了该措施的结构有效性。 [7] 为了解决这个问题， 我们提出了一种新的（共）集成方法来基于两者的因子建模 价格和回报。 [8] （2019）使用双因子建模，仍然缺少关于其跨性别和躯体疾病的测量不变性的证据。 [9] 本文的目的是通过使用响应面法 (RSM) 的 3 级 5 因子建模，研究在鸡蛋壳衍生的 CaO 催化剂上通过牛脂甲醇分解生产生物柴油的最佳条件。 [10] 结果被解释为双因素模型在理解自我伤害的潜在结构方面的效用的初步证据。 [11] 方法 作为大型随机临床试验的一部分，验证性因素分析、双因素模型和结构方程模型 (SEM) 与在治疗前和治疗后收集的数据一起使用。 [12]
exploratory structural equation
Finally, we end our article with a discussion of alternative forms of model specification that have become particularly popular recently: exploratory structural equation modeling (ESEM) and bifactor modeling. [1] The present study with 2,273 students aimed to examine the factorial validity of the Anxiety Questionnaire for Students (AFS) by using the bifactor modeling framework, that is, contrasting a confirmatory factor analysis (CFA) model to an exploratory structural equation model (ESEM) and two bifactor models (B-CFA and B-ESEM). [2] We expand on this work by evaluating the psychometric properties of the instrument, using a combination of exploratory structural equation and bifactor modeling, and item response techniques. [3]最后，我们以讨论最近变得特别流行的模型规范的替代形式来结束我们的文章：探索性结构方程建模 (ESEM) 和双因子建模。 [1] 本研究涉及 2,273 名学生，旨在通过使用双因子建模框架来检验学生焦虑问卷 (AFS) 的因子有效性，即将验证性因子分析 (CFA) 模型与探索性结构方程模型 (ESEM) 和两个双因子模型（B-CFA 和 B-ESEM）。 [2] nan [3]
Latent Factor Modeling
The chapter outlines some key issues in operationalizing constructs - that is, measurement models, including latent factor modeling and measurement invariance. [1] In this paper, we propose a novel generative model called TraLFM via latent factor modeling to mine human mobility patterns underlying traffic trajectories. [2] To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). [3]本章概述了构造操作中的一些关键问题——即测量模型，包括潜在因素建模和测量不变性。 [1] 在本文中，我们提出了一种新的生成模型，称为 TraLFM，通过潜在因子建模来挖掘交通轨迹下的人类移动模式。 [2] nan [3]
Dynamic Factor Modeling
The constructed Investor Sentiment Index for Europe draws upon three well-established and two recent individual sentiment proxies through a novel dynamic factor modeling addressed to behavioral finance. [1] Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analysis and dynamic factor modeling. [2]构建的欧洲投资者情绪指数通过针对行为金融学的新型动态因子模型，利用了三个成熟的和最近的两个个人情绪代理。 [1] nan [2]
factor modeling approach 因子建模方法
Following a parsimonious multi-factor modeling approach, our statistical analyses revealed that increased IL-1Alpha and IL-12/IL-23p40 concentrations were associated with HPV infection. [1] We find fertile ground in applying, for the first time, a factor modeling approach to the Australian port sector by utilizing a disaggregate dataset of 2765 series representing national and regional port activity for 20 years. [2] This study demonstrates the importance of using a multi-factor modeling approach and spectral transformation techniques for estimating the forage P of grasslands and provides a scientific basis for the reasonable use and management of alpine grassland resources. [3]采用简约的多因素建模方法后，我们的统计分析显示 IL-1Alpha 和 IL-12/IL-23p40 浓度增加与 HPV 感染相关。 [1] 我们通过利用代表 20 年国家和地区港口活动的 2765 系列分解数据集，首次将因子建模方法应用于澳大利亚港口部门，我们发现了沃土。 [2] 本研究论证了采用多因子建模方法和光谱变换技术估算草地牧草P的重要性，为高寒草地资源的合理利用和管理提供科学依据。 [3]
factor modeling method
MATERIALS AND METHODS Firstly, the compound factor modeling method with the principle of "indiscipline in diet + excessive fatigue + intragastric administration of Senna water extracts" was used to establish Sprague Dawley (SD) rats as SYD model. [1] Specific objectives: 1) Establish real-time object tracking and spatiotemporal analysis methods for automatically assessing the productivity of field activities and detecting anomalous spatiotemporal relationship among activities that cause inefficiencies and risks; 2) Establish real-time human tracking and spatiotemporal analysis methods and relevant human factor modeling methods for automatically diagnosing ineffective human interactions and unexpected trajectories of workers that cause inefficient team collaborations between AOCC, satellite outage centers, NPP workers, and maintenance service providers; 3) Test the proposed automated object/human tracking and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. [2]材料和方法 首先采用“饮食不规律+过度疲劳+番泻叶灌胃”原则的复合因子建模方法建立Sprague Dawley（SD）大鼠作为SYD模型。 [1] nan [2]