The Explanatory Generalized Graded Unfolding Model: Incorporating Collateral Information to Improve the Latent Trait Estimation Accuracy

2021 ◽  
pp. 014662162110517
Author(s):  
Seang-Hwane Joo ◽  
Philseok Lee ◽  
Stephen Stark
Keyword(s):  
Large Scale ◽  
Latent Trait ◽  
Theory Model ◽  
Estimation Accuracy ◽  
Cognitive Assessments ◽  
Collateral Information ◽  
Unfolding Model ◽  
Generalized Graded Unfolding Model ◽  
Scoring Accuracy ◽  
Trait Estimation

Collateral information has been used to address subpopulation heterogeneity and increase estimation accuracy in some large-scale cognitive assessments. The methodology that takes collateral information into account has not been developed and explored in published research with models designed specifically for noncognitive measurement. Because the accurate noncognitive measurement is becoming increasingly important, we sought to examine the benefits of using collateral information in latent trait estimation with an item response theory model that has proven valuable for noncognitive testing, namely, the generalized graded unfolding model (GGUM). Our presentation introduces an extension of the GGUM that incorporates collateral information, henceforth called Explanatory GGUM. We then present a simulation study that examined Explanatory GGUM latent trait estimation as a function of sample size, test length, number of background covariates, and correlation between the covariates and the latent trait. Results indicated the Explanatory GGUM approach provides scoring accuracy and precision superior to traditional expected a posteriori (EAP) and full Bayesian (FB) methods. Implications and recommendations are discussed.

A Comparison of the LR and DFIT Frameworks of Differential Functioning Applied to the Generalized Graded Unfolding Model

2011 ◽  
Vol 35 (8) ◽  
pp. 623-642 ◽  
Author(s):  
Nathan T. Carter ◽  
Michael J. Zickar
Keyword(s):  
Ideal Point ◽  
Poor Performance ◽  
Theory Model ◽  
Future Research ◽  
Differential Functioning ◽  
Unfolding Model ◽  
Point Conception ◽  
Item Functioning ◽  
Generalized Graded Unfolding Model ◽  
Item Responses

Recently, applied psychological measurement researchers have become interested in the application of the generalized graded unfolding model (GGUM), a parametric item response theory model that posits an ideal point conception of the relationship between latent attributes and observed item responses. Little attention has been given to considerations for the detection of differential item functioning (DIF) under the GGUM. In this article, the authors present a Monte Carlo simulation meant to assess the efficacy of the likelihood ratio (LR) and differential functioning of items and tests (DFIT) frameworks, two popular ways of detecting DIF. Findings indicate a marked superiority of the LR approach over DFIT in terms of true and false positive rates under the GGUM. The discussion centers on possible explanations for the poor performance of the DFIT framework in detecting DIF under the GGUM and addresses limitations of the current study as well as future research directions.

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

scholarly journals Location-Aided Uplink Transmission for User-Centric Cell-Free Massive MIMO Systems: A Fairness Priority Perspective

2022 ◽  
Author(s):  
Chen Wei ◽  
Kui Xu ◽  
Zhexian Shen ◽  
Xiaochen Xia ◽  
Wei Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Estimation Accuracy ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output ◽  
User Centric ◽  
Assignment Scheme

Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme based on a novel proposed metric representing inter-user interference to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which the normalized local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while the normalized maximum ratio (MR) combining is adopted for the remaining APs. A new closed-form SE expression using the normalized MR combining is derived and a novel metric to indicate the UE fairness is also proposed. Moreover, the max-min fairness (MMF) power control algorithm is utilized to further ensure uniformly good service to the UEs. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. Furthermore, although the proposed location-aided uplink combining scheme is not always the best in terms of the per-UE SE, it can provide the more fairness among UEs and can achieve a good trade-off between the average SE and computational complexity.

scholarly journals Modern Subsampling Methods for Large-Scale Least Squares Regression

2020 ◽  
Vol 2 (2) ◽  
pp. 1-28
Author(s):  
Tao Li ◽  
Cheng Meng
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Computing Time ◽  
Real Data ◽  
Optimality Criteria ◽  
Estimation Accuracy ◽  
Least Squares Regression ◽  
Large Scale Data ◽  
Coefficient Estimation ◽  
Sampling Probability

Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are developed for more effective coefficient estimation and model prediction. This review presents some cutting-edge subsampling methods based on the large-scale least squares estimation. Two major families of subsampling methods are introduced: the randomized subsampling approach and the optimal subsampling approach. The former aims to develop a more effective data-dependent sampling probability while the latter aims to select a deterministic subsample in accordance with certain optimality criteria. Real data examples are provided to compare these methods empirically, respecting both the estimation accuracy and the computing time.

scholarly journals COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Han Bao ◽  
Xun Zhou ◽  
Yiqun Xie ◽  
Yingxue Zhang ◽  
Yanhua Li
Keyword(s):  
Spatial Heterogeneity ◽  
Real World ◽  
Large Scale ◽  
Census Data ◽  
Human Mobility ◽  
Training Data ◽  
Estimation Accuracy ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

scholarly journals Towards a unification of niche and neutral models of community ecology

2018 ◽  
Author(s):  
Andres Laan ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Community Dynamics ◽  
Neutral Theory ◽  
Species Abundance ◽  
Theory Model ◽  
Niche Theory ◽  
Species Abundance Distributions ◽  
Restoring Forces ◽  
Neutral Models

AbstractEcological models of community dynamics fall into two main categories. The neutral theory of biodiversity correctly predicts various large-scale ecosystem characteristics such as the species abundance distributions. On a smaller scale, the niche theory of species competition explains population dynamics and interactions between two to a dozen species. Despite the successes of the two theories, they rely on two contradictory assumptions. In the neutral theory each species is competitively equivalent while in the niche theory every species is specialized to exploit a specific part of its environment. Here we propose a resolution to this contradiction using a game theory model of competition with an attractor hyperplane as its equilibrium solution. When the population dynamics shifts within the hyperplane, it is selectively neutral. However, any movement perpendicular to the hyperplane is subject to restoring forces similar to what is predicted by the niche theory. We show that this model correctly reproduces empirical species abundance distributions and is also compatible with species removal experiments.

scholarly journals iCatcher+: Robust and automatic gaze classification of infant webcam videos

2021 ◽  
Author(s):  
Peng Cao ◽  
Xincheng Tan ◽  
Kimberly Megan Scott ◽  
Shari Liu
Keyword(s):  
Large Scale ◽  
Video Quality ◽  
Estimation Accuracy ◽  
Video Frame ◽  
Human Infants ◽  
The Gaze ◽  
Energy Consuming ◽  
The Mind ◽  
Time And Energy

Studies of human infants provide a window into the origins of the mind, and collecting and annotating behavioral data from them remains slow and laborious. Although online platforms enable families to participate in studies via webcam, it remains time- and energy- consuming to manually annotate gaze directions on the collected videos. Existing gaze coding algorithms on videos either suffer from low video quality or still require considerable manual effort. In this project, we built on a promising system for automatic gaze annotation in human infants, iCatcher (Erel et al., 2020), and added two key additional features: a more robust infant face detector, and a gaze estimator that takes into account not only the features of the selected face, but where and how far it is from the screen. In our framework, iCatcher+, all possible faces in a video frame are first extracted by a face extractor, and then the infant face is selected by a infant selector; finally, the gaze direction is classified by a gaze estimator based on both the selected face and features of its bounding box. We evaluated iCatcher+ on a large-scale infant video dataset collected via webcam. The experimental results show improvements in gaze estimation accuracy, compared to the baseline framework. We see iCatcher+ as a key tool for enabling rapid large-scale studies of infant behavior.

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