Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Model validation”

MODEL VALIDATION OUTCOME MODEL VALIDATION

Luca Formaggia, Paradiso Lorenzo Tamellini, Sea Keeping et al. · 2011

Cited by 0

trade-model-validation: Trade model validation v1.0.1

Morland, Christian, Tandetzki, Julia, Schier, Franziska · Zenodo (CERN European Organization for Nuclear Research) · 2025

This repository provides a tool for analyzing trade flows using Gravity Models and Artificial Neural Networks (ANNs) in wood markets. It supports flexible configuration, allowing users to customize input parameters, data selection, and model settings.

Cited by 0Open access

trade-model-validation: Trade model validation v1.0.1

Morland, Christian, Tandetzki, Julia, Schier, Franziska · Zenodo (CERN European Organization for Nuclear Research) · 2025

Cited by 0Open access

Validation of Model Bubbles Model Bubbles Validation

K P Black, G P Jenkins · 2020

Cited by 0Open access

Model Validation

Advances in industrial control · 2007

Cited by 0

Model validation.

Casper Woroszyło (5942669), Boseung Choi (5942672), Jessica Healy Profitós (5942675) et al. · Figshare · 2018

The distributions of the posterior means of the counts of asymptomatic individuals in juvenile (J) and adult compartments based on the fitted SID model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0206418#pone.0206418.e024" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0206418#pone.0206418.e025" target="_blank">3</a>) vs the actual observed values from M = 63 Maroua households (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0206418#pone.0206418.t001" target="_blank">Table 1</a>) marked by vertical lines.

Cited by 0Open access

Model Validation

Antonio Arfé, Paolo Gianturco · RePEc: Research Papers in Economics

Abstract The internal validation of rating systems is part of the general framework for rating system controls. According to both Italian and international regulatory requirements, validation activities should not be confined to empirical validation methods and tests, but should assess the overall functioning of the rating system along different dimensions that include method, the IT system and data quality, and processes and governance. Following the Basel Committee on Banking Supervision guidelines (2006, p.109), to comply with the normative framework, “banks must have a robust system in place to validate the accuracy and consistency of rating systems, processes, and the estimation of all relevant risk components. A bank must demonstrate to its supervisor that the internal validation process enables it to assess the performance of internal rating and risk estimation systems consistently and meaningfully.”

Cited by 0

Model validation.

Edward J. A. Turnham (353804), Daniel A. Braun (131724), Daniel M. Wolpert (118014) · Figshare · 2015

(A) The distribution of the difference in covariance orientation angle between pairs of elements in the generating and fitted priors, aggregated across all six pairings of elements. (B) The corresponding distribution when random priors are compared. (C) The distribution of the absolute difference in correlation coefficient between pairs of elements in the generating and fitted priors, aggregated across all six pairings of elements. (D) The corresponding distribution when random priors are compared.

Cited by 0Open access

Model validation.

Jonah Berger (5197478), Joshua Conrad Jackson (7354166), Ceren Kolsarici (14307622) · Figshare · 2022

Cited by 0Open access

Model validation.

Emilie Lindkvist (3724552), Xavier Basurto (371656), Maja Schlüter (3074016) · Figshare · 2017

Panel a. The number of fishers active in a PC or a co-op in the calibrated model but with annual entry dynamics (i) and different model assumptions (ii and iii). Panel b. The number of co-ops dissolving because of lack of capital or lack of loyalty among members to the co-op. Reliability and fishing skills are equal for all fishers (set to 1.0 and 0.5 respectively), and initial loyalty = 0 for all fishers. Panel a and b are based on the same data. The data represents an average of 500 repetitions.

Cited by 0Open access

Model validation.

Yujing Shi (6010040), Chenyang Wu (5796467) · Figshare · 2023

<div>The "live streaming + charity" model is a new model for China’s philanthropy, accelerating the new development of China’s philanthropy, but there is still a relative paucity of research in the academic community on how charity live streaming affects online charitable donations. In this sense, this study aims to identify the construction of a model of the factors influencing charity live streaming on online charitable donations. This study selected TikTok Live, based on the UTAUT model, combining perceived risk and perceived interactivity, recovered 607 valid questionnaires, and concluded and structural equation modeling to construct an influence factor model to analyze their correlation. The results show that users’ performance expectancy, effort expectancy, perceived interactivity, facilitating conditions, and social influence are significantly positively correlated with online charitable donations, and perceived risk does not negatively affect users’ intentions to make online charitable donations. Our findings can provide a basis for live-streaming platforms and relevant social organizations and government departments to develop charity communication strategies.</div>

Cited by 0Open access

Model validation.

Ian F. Coccimiglio (9188693), David C. Clarke (9188696) · Figshare · 2020

The model predictions were compared to data collected from continuous cycling exercise performed by sedentary (panels A-D) or trained human participants (panels E-H) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008079#pcbi.1008079.ref041" target="_blank">41</a>] and sprint-interval cycling exercise [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008079#pcbi.1008079.ref006" target="_blank">6</a>] (panels I-L). The hatched vertical lines indicate the times at which exercise was initiated and ceased. Data points and error bars represent the means ± standard error. A, E, I) Time courses of phosphocreatine (PCr) concentration. The filled circles represent the measured concentration. B, F, J) Semi-logarithmic plot showing the time courses of adenine nucleotide concentrations in response to exercise. The open and grey-filled circles represent the measured ATP and AMP concentrations, respectively. C, G, K) Time courses of total and phospho-AMPK (p-AMPK) levels in response to exercise. The open circles represent measured p-AMPK levels. D, H, L) Time courses of the simulated kinase activities for each AXP-p-AMPK complex. The open circles represent the mean relative change in total AMPK activities between rest and exercise.

Cited by 0Open access

Model validation.

Ross A. Kelly (6029210), Joseph Leedale (6029213), Andy Harrell (6029216) et al. · Figshare · 2018

Model simulation of PPRgly (solid line) compared to in vitro PPRgly data normalized to protein content. PPRgly data was taken from EFA results (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0207803#pone.0207803.g002" target="_blank">Fig 2</a>) followed by adjusting each ECAR measurement for respiratory contributions. Prior to t = 1440 min, the model is simulated to steady state with extracellular glucose concentration of 25 mM (not shown). At t = 1440 min, extracellular glucose and lactate is removed to replicate in vitro procedure. At t = 1500 min, glucose is reintroduced at 5 mM allowing comparison to experimental data.

Cited by 0Open access

Model Validation

Lou Safra · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Model validation.

Stefan Flasche (225464), Olivier Le Polain de Waroux (101472), Katherine L. O’Brien (462225) et al. · Figshare · 2016

Distributions of the ratio of predicted and observed IRR for each setting assuming full serotype replacement. A value of 1 represents perfect agreement between prediction and observation.

Cited by 0Open access

Model validation.

Claudia M. Rivera-Hoyos (5661991), Edwin David Morales-Álvarez (672408), Sergio Alejandro Poveda-Cuevas (672409) et al. · Figshare · 2015

For all graphs from left to right, first for LaccGluc followed by LaccPost. a. QMEAN distribution and bar graphs. Distribution graphs illustrate QMEAN score tendencies from high-resolution PDB crystallographic structures. Bar graphs help identify “acceptable” or positive scores (blue) from “unacceptable” or negative scores (reed) for each description. b. 3D structure with color gradient from blue to red defining regions with resolutions between 1 and 3.5 Å respectively. The greater blue values represent a more reliable structure. c. Ramachandran plots. We inferred model residues make part of α-helices and β-sheet and beta turns since they occupy permitted spaces. Red points correspond to amino acids different than glycine and proline. d. Ball-and-stick model between the model and its template, DD matrices estimated by SuperPose. G. lucidum and P. ostreatus models in orange, 1GYC structure in green. Overlapping was visualized by Chimera. DD matrices demonstrated the comparison between model principal chains (x-axis) and the template (y-axis). Differences between 0 and 1.5 Å in white, between 1.5 and 3 Å in yellow, between 3 and 5 Å in light green, between 5 and 7 Å in turquoise, between 7 and 9 Å in dark blue and over 9 Å in black i.e. the darker the color the greater the distance difference between structures.

Cited by 0Open access

Model validation.

Leandro Calado (6978689), Bernardo Cosenza (17636191), Francisco Moraes (17636194) et al. · Figshare · 2025

Comparative T–S diagram showing data points from the ROMS model (black markers) and ARGO profilers (blue markers), covering the period from 2010 to 2019 (PDF)

Cited by 0Open access

Model validation.

Julia Deichmann (11297724), Sara Bachmann (11297727), Marie-Anne Burckhardt (11297730) et al. · Figshare · 2023

Data (mean ± SEM, n = 6) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010289#pcbi.1010289.ref047" target="_blank">47</a>] and model predictions for validation study (1). PA sessions are marked by vertical lines. PA was performed at different intensities (25%, 50% and 75% ) for durations of 30 or 60 min. A meal was consumed 90 min prior to PA, with a meal insulin bolus u of 100%, 50% or 25% of the full dose. The shaded areas show the 95% prediction intervals of the T1D model (V1), and solid BG trajectories display the tuned model (b = 1.83 ⋅ 10−6, q1 = 2.93 ⋅ 10−6, q2 = 2.93 ⋅ 10−6). Meal parameters f and τm = 105 min were determined from glucose levels at rest.

Cited by 0Open access

Model validation.

Sunny Eloot (138527), Daniel Schneditz (504840), Tom Cornelis (847348) et al. · Figshare · 2016

Correlation (R = 0.942; P<0.001) between the reduction ratios as measured in vivo and those as derived from mathematical analyses for the different protein-bound solutes [PCG (green), HA (orange), IAA (blue), IS (red), and PCS (black)] and the different dialysis strategies [4h HD (open diamonds) and 8h HD (filled diamonds) for correlation with the study of Meijers et al.; low KOA (open circles) and high KOA (filled circles) for correlation with the study by Sirich et al.; 4h hemodialysis HD (open triangles), 8h HD (closed triangles), 4h hemodiafiltration HDF (open squares), and 8h HDF (filled squares) for correlation with our own in vivo data].

Cited by 0Open access

Model validation.

Matias I. Maturana (2586511), Nicholas V. Apollo (2586502), Alex E. Hadjinicolaou (2586505) et al. · Figshare · 2016

(a) The predicted probability compared to the actual probability from the validation data for all cells (gray). For clarity this data is shown without error bars. The validation of the sample cell from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004849#pcbi.1004849.g007" target="_blank">Fig 7</a> with standard error bars (black solid line) is also shown. The root mean square error for this cell was 0.056. (b) The 2-dimensional model error (ERMS2) is compared to the 1-dimensional model error (ERMS1) for all cells where the hypothesis test recovered 2 or more significant components.

Cited by 0Open access