Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The robust estimation of examinee ability based on the four-parameter logistic model when guessing and carelessness responses exist.

ABSTRACT: The three-parameter Logistic model (3PLM) and the four-parameter Logistic model (4PLM) have been proposed to reduce biases in cases of response disturbances, including random guessing and carelessness. However, they could also influence the examinees who do not guess or make careless errors. This paper proposes a new approach to solve this problem, which is a robust estimation based on the 4PLM (4PLM-Robust), involving a critical-probability guessing parameter and a carelessness parameter. This approach is compared with the 2PLM-MLE(two-parameter Logistic model and a maximum likelihood estimator), the 3PLM-MLE, the 4PLM-MLE, the Biweight estimation and the Huber estimation in terms of bias using an example and three simulation studies. The results show that the 4PLM-Robust is an effective method for robust estimation, and its calculation is simpler than the Biweight estimation and the Huber estimation.

SUBMITTER: Jian X

PROVIDER: S-EPMC8084210 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

The robust estimation of examinee ability based on the four-parameter logistic model when guessing and carelessness responses exist.

Jian Xiaozhu X Buyun Dai D Yuanping Deng D

PloS one 20210429 4

The three-parameter Logistic model (3PLM) and the four-parameter Logistic model (4PLM) have been proposed to reduce biases in cases of response disturbances, including random guessing and carelessness. However, they could also influence the examinees who do not guess or make careless errors. This paper proposes a new approach to solve this problem, which is a robust estimation based on the 4PLM (4PLM-Robust), involving a critical-probability guessing parameter and a carelessness parameter. This ...[more]

PMID: 33914784

Similar Datasets

Bayesian Modal Estimation for the One-Parameter Logistic Ability-Based Guessing (1PL-AG) Model.

Project description:The calibration of the one-parameter logistic ability-based guessing (1PL-AG) model in item response theory (IRT) with a modest sample size remains a challenge for its implausible estimates and difficulty in obtaining standard errors of estimates. This article proposes an alternative Bayesian modal estimation (BME) method, the Bayesian Expectation-Maximization-Maximization (BEMM) method, which is developed by combining an augmented variable formulation of the 1PL-AG model and a mixture model conceptualization of the three-parameter logistic model (3PLM). By comparing with marginal maximum likelihood estimation (MMLE) and Markov Chain Monte Carlo (MCMC) in JAGS, the simulation shows that BEMM can produce stable and accurate estimates in the modest sample size. A real data example and the MATLAB codes of BEMM are also provided.

| S-EPMC8042557 | biostudies-literature

Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model

Project description:The four-parameter logistic (4PL) model has recently attracted much interest in educational testing and psychological measurement. This paper develops a new Gibbs-slice sampling algorithm for estimating the 4PL model parameters in a fully Bayesian framework. Here, the Gibbs algorithm is employed to improve the sampling efficiency by using the conjugate prior distributions in updating asymptote parameters. A slice sampling algorithm is used to update the 2PL model parameters, which overcomes the dependence of the Metropolis–Hastings algorithm on the proposal distribution (tuning parameters). In fact, the Gibbs-slice sampling algorithm not only improves the accuracy of parameter estimation, but also enhances sampling efficiency. Simulation studies are conducted to show the good performance of the proposed Gibbs-slice sampling algorithm and to investigate the impact of different choices of prior distribution on the accuracy of parameter estimation. Based on Markov chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarginal likelihood are considered to assess the model fittings. Moreover, a detailed analysis of PISA data is carried out to illustrate the proposed methodology.

| S-EPMC7530206 | biostudies-literature

Nonlinear Calibration Model Choice between the Four and Five-Parameter Logistic Models.

Project description:Both the four-parameter logistic (4PL) and the five-parameter logistic (5PL) models are widely used in nonlinear calibration. In this paper, we study the choice between 5PL and 4PL both by the accuracy and precision of the estimated concentrations and by the power to detect an association between a binary disease outcome and the estimated concentrations. Our results show that when the true curve is symmetric around its inflection point, the efficiency loss from using 5PL is negligible under the prevalent experimental design. When the true curve is asymmetric, 4PL may sometimes offer better performance due to bias-variance trade-off. We provide a practical guideline for choosing between 5PL and 4PL and illustrate its application with a real dataset from the HIV Vaccine Trials Network laboratory.

| S-EPMC4263697 | biostudies-literature

Parameter estimation for robust HMM analysis of ChIP-chip data.

Project description:BackgroundTiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis.ResultsHere we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure.ConclusionWe illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.

| S-EPMC2536674 | biostudies-literature

Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.

Project description:PurposeFor quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps.TheoryFrom a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP).MethodsWe compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE.ResultsOn the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters.ConclusionAMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

| S-EPMC10664815 | biostudies-literature

A Two-Parameter Logistic Extension Model: <i>An Efficient Variant of the Three-Parameter Logistic Model</i>.

Project description:A three-parameter logistic (3PL) model variant, named the two-parameter logistic extension (2PLE) model, was developed. This new model employs a function that integrates item features according to an examinee's ability level instead of a fixed guessing parameter used in the 3PL model to quantify guessing behavior. Correct response probabilities from a solution behavior and guessing behavior increase as the level of ability increases. At extreme cases in which the level of ability is close to negative infinity, the 2PLE model degenerates into a 3PL model with a guessing probability at chance level (i.e., 1/m, where m is the number of options). The properties of the 2PLE model were described and compared with those of other guessing models. Then, a simulation study comparing the performance of the 2PLE model with that of the 3PL model under three scenarios was conducted. Results showed that the 2PLE model generally outperforms the 3PL model. Finally, the application of the new model in comparison with several existing models was demonstrated by using two real data sets.

| S-EPMC6696869 | biostudies-literature

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

Project description:PurposeThis study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions.MethodsA long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves.ResultsThe two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to -2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method.ConclusionsThe parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity.Translational relevanceDark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.

| S-EPMC9631495 | biostudies-literature

Assessing the Accuracy of Parameter Estimates in the Presence of Rapid Guessing Misclassifications.

Project description:The presence of rapid guessing (RG) presents a challenge to practitioners in obtaining accurate estimates of measurement properties and examinee ability. In response to this concern, researchers have utilized response times as a proxy of RG and have attempted to improve parameter estimation accuracy by filtering RG responses using popular scoring approaches, such as the effort-moderated item response theory (EM-IRT) model. However, such an approach assumes that RG can be correctly identified based on an indirect proxy of examinee behavior. A failure to meet this assumption leads to the inclusion of distortive and psychometrically uninformative information in parameter estimates. To address this issue, a simulation study was conducted to examine how violations to the assumption of correct RG classification influences EM-IRT item and ability parameter estimation accuracy and compares these results with parameter estimates from the three-parameter logistic (3PL) model, which includes RG responses in scoring. Two RG misclassification factors were manipulated: type (underclassification vs. overclassification) and rate (10%, 30%, and 50%). Results indicated that the EM-IRT model provided improved item parameter estimation over the 3PL model regardless of misclassification type and rate. Furthermore, under most conditions, increased rates of RG underclassification were associated with the greatest bias in ability parameter estimates from the EM-IRT model. In spite of this, the EM-IRT model with RG misclassifications demonstrated more accurate ability parameter estimation than the 3PL model when the mean ability of RG subgroups did not differ. This suggests that in certain situations it may be better for practitioners to (a) imperfectly identify RG than to ignore the presence of such invalid responses and (b) select liberal over conservative response time thresholds to mitigate bias from underclassified RG.

| S-EPMC8725050 | biostudies-literature

Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.

Project description:Parameter estimation is one of the central challenges in computational biology. In this paper, we present an approach to estimate model parameters and assess their identifiability in cases where only partial knowledge of the system structure is available. The partially known model is embedded into a system of hybrid neural ordinary differential equations, with neural networks capturing unknown system components. Integrating neural networks into the model presents two main challenges: global exploration of the mechanistic parameter space during optimization and potential loss of parameter identifiability due to the neural network flexibility. To tackle these challenges, we treat biological parameters as hyperparameters, allowing for global search during hyperparameter tuning. We then conduct a posteriori identifiability analysis, extending a well-established method for mechanistic models. The pipeline performance is evaluated on three test cases designed to replicate real-world conditions, including noisy data and limited system observability.

| S-EPMC11604934 | biostudies-literature

Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock clustering models.

Project description:In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.

| S-EPMC5575305 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data