## What is/are Flexible Modeling?

Flexible Modeling - The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory.^{[1]}Flexible modeling of the return asymmetry and fat tails provides an accurate forecast of the value-at-risk and expected shortfall and contributes to the analysis of downside risk in the FX market.

^{[2]}Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior requirement.

^{[3]}SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches.

^{[4]}For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories.

^{[5]}The vine copula model is used to enable a flexible modeling of multiple nonlinear dependencies by mapping the linear correlations into the non-Gaussian copula parameters.

^{[6]}165 (2005) 157–190 Does the new version supersede the previous version?: Yes Reasons for the new version: This new version offers increased accuracy, and a more flexible modeling of elastic collisions of electrons and positrons with atoms in elemental solids.

^{[7]}Fuzzy integrations improve DM models in five principal features: (1) expert knowledge, (2) uncertainty handling, (3) human and government behavior modeling, (4) flexible modeling, and (5) simpler representations.

^{[8]}Partial Least Squares path modeling was preferred for research studies for its flexible modeling and identifying key drivers.

^{[9]}Flexible modeling of the relationship between PSA and each outcome did not support dichotomization at a threshold of 10 ng/ml.

^{[10]}Flexible modeling of the shape of BMI distributions may improve prediction performance.

^{[11]}In this paper, we elaborate features related to flexible modeling that we have identified, and show how these features were realized in our approach.

^{[12]}In this author, the paper present OpenEHR, a consistent health standard based on the dual-level scheme, which separates the reference model from the archetypes, allowing a flexible modeling of clinical concepts.

^{[13]}First, a novel multi-level, multi-layer, multi-agent approach is proposed to enable flexible modeling of the interconnected systems.

^{[14]}Summary We present flexiMAP (flexible Modeling of Alternative PolyAdenylation), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data.

^{[15]}Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

^{[16]}Integrating both the suitable characteristics of partial least squares (PLS) and adaptive neural network fuzzy inference systems (ANFIS) procedure, PLS-ANFIS model is employed to allow for flexible modeling of multivariable nonlinear processes.

^{[17]}The proposed measure shows better robustness to noise and allows a more flexible modeling of vagueness with respect to the Fuzzy Entropy.

^{[18]}However, Inquiry Play promoted more emergent, flexible modeling of underlying mechanisms while Game Play oriented students more towards “winning”.

^{[19]}We introduce a semiparametric smooth coefficient estimator for recreation demand data that allows more flexible modeling of preference heterogeneity.

^{[20]}Their method combines ordinary differential equation-based (ODE-based) mechanistic modeling of ILI spread with flexible modeling of discrepancies between the ODE trajectories and observed incidence.

^{[21]}Our approach allows for flexible modeling of temporally changing intervention effects while also borrowing strength in estimation over time.

^{[22]}&NA; We propose a novel approach for the flexible modeling of complex exposure‐lag‐response associations in time‐to‐event data, where multiple past exposures within a defined time window are cumulatively associated with the hazard.

^{[23]}There is extensive work in this area, however, current methods have difficulty with one or more of the following: resolving activity of TFs with overlapping regulons, reflecting known regulatory relationships, or flexible modeling of TF activity over the regulon.

^{[24]}This paper proposes an inferential LDA method to efficiently obtain unbiased estimates under flexible modeling for heterogeneous text corpora with the method of partial collapse and the Dirichlet process mixtures.

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## Allow Flexible Modeling

In this chapter, the authors describe a novel generic approach and its realization for the development of intelligent systems that allow flexible modeling of ethical and legal aspects.^{[1]}Our methods allow flexible modeling of protein–gene relationships as well as induces sparsity in both protein–gene and protein–survival relationships, to select genomically driven prognostic protein markers at the patient-level.

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## Allowed Flexible Modeling

Quintiles and restricted cubic splines allowed flexible modeling of the HRs in unadjusted and multivariable-adjusted Cox regression models.^{[1]}GAMLSS allowed flexible modeling of both the distribution of the dry matter yield from B.

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## New Flexible Modeling

To overcome the limitation on the topological structure, we propose a new flexible modeling method to the framework so that we can specify a topological substructure of graphs and a partial assignment of chemical elements and bond-multiplicity to a target graph.^{[1]}The direct coupling of introducing multi-industry systems - tools, 3D databases, AEC, and Open-BIM technologies opens up totally new ways of approaching architectural design problems resulting in a new flexible modeling workflow with real-time visualization.

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## flexible modeling approach

Conclusions Finite mixture models offer a flexible modeling approach that has benefits over standard parametric models when analyzing heterogenous data for estimating survival times needed for cost-effectiveness analysis.^{[1]}Here, we develop a more flexible modeling approach with coupled population balance models that move both material and stress across the molecular weight distribution.

^{[2]}BACKGROUND AND OBJECTIVE Our goal is to provide an overall strategy for utilizing continuous accelerated life models in the discrete setting that provides a unique and flexible modeling approach across a variety of hazard shapes.

^{[3]}A flexible modeling approach with finite impulse response basis functions was employed since non-canonical hemodynamic response was expected.

^{[4]}The results on real data show the importance of the flexible modeling approach provided by the proposed model.

^{[5]}We proposed a flexible modeling approach that incorporated local monitoring data with spatial data to predict the spatial characteristics of the marine fisheries in Tanzania.

^{[6]}The proposed approach involves simple mathematics and provides a flexible modeling approach close to human reasoning that is able to consider uncertain information and relationships of arbitrary complexity.

^{[7]}The result is a flexible modeling approach that can be used for data exploration in a large variety of problems.

^{[8]}From an expert and intelligent systems point of view, the study provides decision makers with a general and flexible modeling approach for assisting them in the formulation of M&A strategy.

^{[9]}The result is a flexible modeling approach that can be used for data exploration in a large variety of problems.

^{[10]}This paper provides a computationally efficient, compact, and flexible modeling approach for describing nonlinear Froude–Krylov forces for axisymmetric wave energy devices, in 6-DoFs.

^{[11]}This flexible modeling approach can easily be adapted to suit sampling designs from numerous species which may be encountered during and outside of discrete breeding seasons.

^{[12]}Moreover, the proposed formulation is parametrized using the signal and channel correlation matrices to account for different waveform and sensor placement designs, thereby allowing a flexible modeling approach.

^{[13]}However, while providing a very flexible modeling approach of polytomous responses, it involves the estimation of many parameters at the risk of numerical instability and overfitting.

^{[14]}Fuzzy modeling, a flexible modeling approach, is applied on the experimental data set because of the small sized data set and diffulty of satisfying probabilistic modeling assumptions.

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## flexible modeling framework

This calls for a flexible modeling framework to yield efficient survival prediction.^{[1]}Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability.

^{[2]}Our goals were to inform planning at our own university, Emory University, a medium-sized university with around 15,000 students and 15,000 faculty and staff, and to provide a flexible modeling framework to inform the planning efforts at similar academic institutions.

^{[3]}As such, they offer a flexible modeling framework that has been applied to many areas, including biology and pharmacology -- most recently, in the fight against COVID-19.

^{[4]}We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: population group characteristics and the specific activities that individuals engage in during the normal course of a day.

^{[5]}We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: population group characteristics and the specific activities that individuals engage in during the normal course of a day.

^{[6]}A highly flexible modeling framework's characteristics allow policymakers to develop the objectives and resources within the constraints of planning vector control programs and case monitoring strategy adjustments.

^{[7]}The simulation experiments have been carried out by a flexible modeling framework that can be adopted by project experts to design construction schedules subject to the uncertainty associated with the multiple resource failure.

^{[8]}In this paper, we present generalized additive models for location scale and shape (GAMLSS) for normative modeling of neuroimaging data, a flexible modeling framework that can model heteroskedasticity, non-linear effects of variables, and hierarchical structure of the data.

^{[9]}This study describes and documents the Simulation of CUAS Networks and Sensors (SCANS) Framework in a novel attempt at developing a flexible modeling framework for CUAS systems based on device parameters.

^{[10]}Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions.

^{[11]}Stochastic programming offers a flexible modeling framework for optimal decision-making problems under uncertainty.

^{[12]}In conclusion, the demographic approach presented here provides a flexible modeling framework that is readily applied to other stream systems and species by adjusting or transferring, when appropriate, species vital rates and flow-event thresholds.

^{[13]}Stochastic dynamic programming provides a flexible modeling framework with which to explore these trade-offs, but this method has not yet been used to study possible changes in optimal trade-offs caused by climate change.

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## flexible modeling technique

Using synthetic data these flexible modeling techniques yield precise parameter estimates.^{[1]}We applied a flexible modeling technique capable of representing dynamics of large populations interacting in space and time, namely Markovian Agents, to study the evolution of COVID-19 in Italy.

^{[2]}CONCLUSIONS Random forest modeling is a more flexible modeling technique than linear regression techniques, and therefore, can identify under-explored risk factors.

^{[3]}CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning.

^{[4]}io/spinemetssurvival/ CONCLUSIONS Preoperative estimation of 90-day and one-year mortality was achieved with assessment of more flexible modeling techniques such as machine learning.

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## flexible modeling method

To overcome the limitation on the topological structure, we propose a new flexible modeling method to the framework so that we can specify a topological substructure of graphs and a partial assignment of chemical elements and bond-multiplicity to a target graph.^{[1]}Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate discovery of robust biomarkers that underlie disease regulatory processes.

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## flexible modeling strategy

Our study shows that methods based on fractional polynomials offer a flexible modeling strategy in most applications.^{[1]}We propose a robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection.

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## flexible modeling language

Using a flexible modeling language, networks, attacks, and defenses are described in high detail, yielding a fine-grained scenario definition.^{[1]}We argue that CP can be effectively used to address this kind of problems because it provides very expressive and flexible modeling languages which come with powerful solvers, thus providing efficient and scalable performance.

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## flexible modeling structure

This revealed a flexible modeling structure adaptable both to cell performance variations and the limitations of the available test data.^{[1]}Among these, the Realized GARCH model provides the best pricing performance due to its fewer constraints and a more flexible modeling structure.

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## flexible modeling system

In CM2Mc, we replace the simple land surface model LaD (where vegetation is static and prescribed) with LPJmL5 and fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS).^{[1]}In CM2Mc, we replace the simple land-surface model LaD (Land Dynamics; where vegetation is static and prescribed) with LPJmL5, and we fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS).

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