Modeling Strategy Guidelines

2010 ◽  
pp. 165-202 ◽  
Author(s):  
David G. Kleinbaum ◽  
Mitchel Klein
Keyword(s):  
Modeling Strategy

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

A standardized modeling strategy for heat current method-based analysis and simulation of thermal systems

Energy ◽  
2021 ◽  
Vol 217 ◽  
pp. 119403
Author(s):  
Tian Zhao ◽  
Xi Chen ◽  
Ke-Lun He ◽  
Qun Chen
Keyword(s):  
Current Method ◽  
Heat Current ◽  
Thermal Systems ◽  
Modeling Strategy

scholarly journals Multiscale modeling strategy to solve fullerene formation mystery

2021 ◽  
pp. 1-12
Author(s):  
Andrey M. Popov ◽  
Irina V. Lebedeva ◽  
Sergey A. Vyrko ◽  
Nikolai A. Poklonski
Keyword(s):  
Multiscale Modeling ◽  
Fullerene Formation ◽  
Modeling Strategy

scholarly journals An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

2020 ◽  
Author(s):  
Chih-Hsiang Yang ◽  
Jaclyn P Maher ◽  
Aditya Ponnada ◽  
Eldin Dzubur ◽  
Rachel Nordgren ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Wearable Sensors ◽  
Activity Levels ◽  
Two Stage ◽  
Modeling Approach ◽  
Source Program ◽  
Modeling Strategy ◽  
Stage Modeling

Abstract People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

scholarly journals A Practical Finite Element Modeling Strategy to Capture Cracking and Crushing Behavior of Reinforced Concrete Structures

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 506 ◽  
Author(s):  
Alexandre Mathern ◽  
Jincheng Yang
Keyword(s):  
Finite Element ◽  
Reinforced Concrete ◽  
Constitutive Relations ◽  
Fe Analysis ◽  
Rc Beams ◽  
Cracking Behavior ◽  
Concrete Cracking ◽  
Rc Structures ◽  
Nonlinear Fe Analysis ◽  
Modeling Strategy

Nonlinear finite element (FE) analysis of reinforced concrete (RC) structures is characterized by numerous modeling options and input parameters. To accurately model the nonlinear RC behavior involving concrete cracking in tension and crushing in compression, practitioners make different choices regarding the critical modeling issues, e.g., defining the concrete constitutive relations, assigning the bond between the concrete and the steel reinforcement, and solving problems related to convergence difficulties and mesh sensitivities. Thus, it is imperative to review the common modeling choices critically and develop a robust modeling strategy with consistency, reliability, and comparability. This paper proposes a modeling strategy and practical recommendations for the nonlinear FE analysis of RC structures based on parametric studies of critical modeling choices. The proposed modeling strategy aims at providing reliable predictions of flexural responses of RC members with a focus on concrete cracking behavior and crushing failure, which serve as the foundation for more complex modeling cases, e.g., RC beams bonded with fiber reinforced polymer (FRP) laminates. Additionally, herein, the implementation procedure for the proposed modeling strategy is comprehensively described with a focus on the critical modeling issues for RC structures. The proposed strategy is demonstrated through FE analyses of RC beams tested in four-point bending—one RC beam as reference and one beam externally bonded with a carbon-FRP (CFRP) laminate in its soffit. The simulated results agree well with experimental measurements regarding load-deformation relationship, cracking, flexural failure due to concrete crushing, and CFRP debonding initiated by intermediate cracks. The modeling strategy and recommendations presented herein are applicable to the nonlinear FE analysis of RC structures in general.

scholarly journals Ambiguity in European seasonal comparative research: how decisions on modelling shape results on inequality in learning?

2021 ◽  
Author(s):  
Laura A. Helbling ◽  
Martin J. Tomasik ◽  
Urs Moser
Keyword(s):  
Comparative Research ◽  
Social Performance ◽  
Real Data ◽  
Social Disparities ◽  
Conditional Learning ◽  
Summer Setback ◽  
Research Questions ◽  
Learning Progress ◽  
Modeling Strategy ◽  
Study Designs

AbstractSummer break study designs are used in educational research to disentangle school from non-school contributions to social performance gaps. The summer breaks provide a natural experimental setting that allows for the measurement of learning progress when school is not in session, which can help to capture the unfolding of social disparities in learning that are the result of non-school influences. Seasonal comparative research has a longer tradition in the U.S. than in Europe, where it is only at its beginning. As such, summer setback studies in Europe lack a common methodological framework, impairing the possibility to draw lines across studies because they differ in their inherent focus on social inequality in learning progress. This paper calls for greater consideration of the parameterization of “unconditional” or “conditional” learning progress in European seasonal comparative research. Different approaches to the modelling of learning progress answer different research questions. Based on real data and constructed examples, this paper outlines in an intuitive fashion the different dynamics in inequality that may be simultaneously present in the survey data and distinctly revealed depending on whether one or the other modeling strategy of learning progress is chosen. An awareness of the parameterization of learning progress is crucial for an accurate interpretation of the findings and their international comparison.

scholarly journals Modeling Near-Miscible Gas Foam Injection in Fractured Tight Rocks and Its Challenges

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1998
Author(s):  
Haishan Luo ◽  
Kishore K. Mohanty
Keyword(s):  
Oil Recovery ◽  
Fractured Rock ◽  
Gas Injection ◽  
Gas Composition ◽  
Gas Oil ◽  
Rock Matrix ◽  
Reducing Gas ◽  
Tight Reservoirs ◽  
Modeling Strategy ◽  
Tight Rocks

Unlocking oil from tight reservoirs remains a challenging task, as the existence of fractures and oil-wet rock surfaces tends to make the recovery uneconomic. Injecting a gas in the form of a foam is considered a feasible technique in such reservoirs for providing conformance control and reducing gas-oil interfacial tension (IFT) that allows the injected fluids to enter the rock matrix. This paper presents a modeling strategy that aims to understand the behavior of near-miscible foam injection and to find the optimal strategy to oil recovery depending on the reservoir pressure and gas availability. Corefloods with foam injection following gas injection into a fractured rock were simulated and history matched using a compositional commercial simulator. The simulation results agreed with the experimental data with respect to both oil recovery and pressure gradient during both injection schedules. Additional simulations were carried out by increasing the foam strength and changing the injected gas composition. It was found that increasing foam strength or the proportion of ethane could boost oil production rate significantly. When injected gas gets miscible or near miscible, the foam model would face serious challenges, as gas and oil phases could not be distinguished by the simulator, while they have essentially different effects on the presence and strength of foam in terms of modeling. We provide in-depth thoughts and discussions on potential ways to improve current foam models to account for miscible and near-miscible conditions.

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