scholarly journals Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment

2021 ◽  
Author(s):  
David Izydorczyk ◽  
Arndt Bröder
Keyword(s):  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Predictive Performance ◽  
Model Fit ◽  
Parameter Estimates ◽  
Data Sets ◽  
Parameter Recovery ◽  
Exemplar Models ◽  
Modeling Procedure ◽  
Direct Recall

AbstractExemplar models are often used in research on multiple-cue judgments to describe the underlying process of participants’ responses. In these experiments, participants are repeatedly presented with the same exemplars (e.g., poisonous bugs) and instructed to memorize these exemplars and their corresponding criterion values (e.g., the toxicity of a bug). We propose that there are two possible outcomes when participants judge one of the already learned exemplars in some later block of the experiment. They either have memorized the exemplar and their respective criterion value and are thus able to recall the exact value, or they have not learned the exemplar and thus have to judge its criterion value, as if it was a new stimulus. We argue that psychologically, the judgments of participants in a multiple-cue judgment experiment are a mixture of these two qualitatively distinct cognitive processes: judgment and recall. However, the cognitive modeling procedure usually applied does not make any distinction between these processes and the data generated by them. We investigated potential effects of disregarding the distinction between these two processes on the parameter recovery and the model fit of one exemplar model. We present results of a simulation as well as the reanalysis of five experimental data sets showing that the current combination of experimental design and modeling procedure can bias parameter estimates, impair their validity, and negatively affect the fit and predictive performance of the model. We also present a latent-mixture extension of the original model as a possible solution to these issues.

scholarly journals Cognitive Modeling in Intelligence Research: Advantages and Recommendations for their Application

2018 ◽  
Author(s):  
Gidon T. Frischkorn ◽  
Anna-Lena Schubert
Keyword(s):  
Working Memory ◽  
Cognitive Processes ◽  
Processing Speed ◽  
Cognitive Modeling ◽  
Cognitive Process ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Models ◽  
Model Fit ◽  
General Intelligence

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive process that may help overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

scholarly journals Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application

2018 ◽  
Vol 6 (3) ◽  
pp. 34 ◽  
Author(s):  
Gidon Frischkorn ◽  
Anna-Lena Schubert
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Processing Speed ◽  
Cognitive Modeling ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Models ◽  
Model Fit ◽  
General Intelligence

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

scholarly journals Kumaraswamy Generalized Power Lomax Distributionand Its Applications

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 28-45
Author(s):  
Vasili B.V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Hazard Rate ◽  
Real Data ◽  
Rate Function ◽  
Maximum Likelihood Estimates ◽  
Parameter Estimates ◽  
Parameter Distribution ◽  
Data Sets ◽  
Lomax Distribution ◽  
Entropy Measures ◽  
Modeling Behavior

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.

scholarly journals The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

2017 ◽  
Vol 10 (4) ◽  
pp. 1679-1701 ◽  
Author(s):  
Silvia Caldararu ◽  
Drew W. Purves ◽  
Matthew J. Smith
Keyword(s):  
Environmental Changes ◽  
Crop Yields ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Model Performance ◽  
Predictive Performance ◽  
Crop Model ◽  
Model Fit ◽  
General Process ◽  
Fitting Algorithm

Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data–space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

scholarly journals Machine learning-based prediction system for rainfall-induced landslides in Benguet First Engineering District

2019 ◽  
Author(s):  
Zanya Reubenne D. Omadlao ◽  
Nica Magdalena A. Tuguinay ◽  
Ricarido Maglaqui Saturay
Keyword(s):  
Machine Learning ◽  
Daily Rainfall ◽  
Predictive Performance ◽  
Data Sets ◽  
Prediction System ◽  
True Positive ◽  
Rainfall Thresholds ◽  
Cumulative Rainfall ◽  
Testing Data ◽  
Positive Rate

A machine learning-based prediction system for rainfall-induced landslides in Benguet First Engineering District is proposed to address the landslide risk due to the climate and topography of Benguet province. It is intended to improve the decision support system for road management with regards to landslides, as implemented by the Department of Public Works and Highways Benguet First District Engineering Office. Supervised classification was applied to daily rainfall and landslide data for the Benguet First Engineering District covering the years 2014 to 2018 using scikit-learn. Various forms of cumulative rainfall values were used to predict landslide occurrence for a given day. Following typical machine learning workflows, rainfall-landslide data set was divided into training and testing data sets. Machine learning algorithms such as K-Nearest Neighbors, Gaussian Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and AdaBoost were trained using the training data sets, and the trained models were used to make predictions based on the testing data sets. Predictive performance of the models vis-a-vis the testing data sets were compared using true positive rates, false positive rates, and the area under the Receiver Operating Characteristic Curve. Predictive performance of these models were then compared to 1-day cumulative rainfall thresholds commonly used for landslide predictions. Among the machine learning models evaluated, Gaussian Naïve Bayes has the best performance, with mean false positive rate, true positive rate and area under the curve scores of 7%, 76%, and 84% respectively. It also performs better than the 1-day cumulative rainfall thresholds. This research demonstrates the potential of machine learning for identifying temporal patterns in rainfall-induced landslides using minimal data input -- daily rainfall from a single synoptic station, and highway maintenance records. Such an approach may be tested and applied to similar problems in the field of disaster risk reduction and management.

scholarly journals Maximum Product of Spacing Parameter Estimation of Gompertz Rayleigh Distribution and Application to Rainfall Datasets

2021 ◽  
pp. 41-59
Author(s):  
Hussein Ahmad Abdulsalam ◽  
Sule Omeiza Bashiru ◽  
Alhaji Modu Isa ◽  
Yunusa Adavi Ojirobe
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Parameter Estimates ◽  
Data Sets ◽  
Unknown Parameters ◽  
Exponentiated Weibull ◽  
Integrated Hazard

Gompertz Rayleigh (GomR) distribution was introduced in an earlier study with few statistical properties derived and parameters estimated using only the most common traditional method, Maximum Likelihood Estimation (MLE). This paper aimed at deriving more statistical properties of the GomR distribution, estimating the three unknown parameters via a competitive method, Maximum Product of Spacing (MPS) and evaluating goodness of fit using rainfall data sets from Nigeria, Malaysia and Argentina. Properties of statistical distributions including distribution of smallest and largest order statistics, cumulative or integrated hazard function, odds function, rth non-central moments, moment generating function, mean, variance and entropy measures for GomR distribution were explicitly derived. The fitted data sets reveal the flexibility of GomR distribution over other distributions been compared with. Simulation study was used to evaluate the consistency, accuracy and unbiasedness of the GomR distribution parameter estimates obtained from the method of MPS. The study found that GomR distribution could not provide a better fit for Argentine rainfall data but it was the best distribution for the rainfall data sets from Nigeria and Malaysia in comparison with the distributions; Generalized Weibull Rayleigh (GWR), Exponentiated Weibull Rayleigh (EWR), Type (II) Topp Leone Generalized Inverse Rayleigh (TIITLGIR), Kumarawamy Exponential Inverse Raylrigh (KEIR), Negative Binomial Marshall-Olkin Rayleigh (NBMOR) and Exponentiated Weibull (EW). Furthermore, the estimates from MPSE were consistent as the sample size increases but not as efficient as those from MLE.

Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process

2021 ◽  
pp. 107699862199436
Author(s):  
Yue Liu ◽  
Hongyun Liu
Keyword(s):  
True Positive Rate ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Detection Accuracy ◽  
Purification Process ◽  
Parameter Recovery ◽  
Residual Method ◽  
Data Contamination ◽  
Item Parameters ◽  
The Impact

The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.

Calibration of three resuspension/sedimentation models

1998 ◽  
Vol 37 (3) ◽  
pp. 41-49 ◽  
Author(s):  
Gerard Blom ◽  
R. Hans Aalderink
Keyword(s):  
Suspended Solids ◽  
Statistical Evaluation ◽  
Model Fit ◽  
Parameter Estimates ◽  
Flume Experiments ◽  
Parameter Estimations ◽  
In Situ Observations ◽  
Squared Residuals ◽  
Very High

Three resuspension and sedimentation models (Blom, Lick and Partheniades and Krone) are calibrated and evaluated on data from flume experiments with sediments from Lake Ketel and in situ suspended solids measurements. We applied a formal parameter estimation technique in combination with a statistical evaluation of the model fit and parameter estimates. All three models produce a reasonable reconstruction of the data from the flume experiment and the in situ observations. The differences in the model fit of the three models are small, except for the in situ observations. Here the sum of squared residuals for Partheniades and Krone's is about twice the sum for Blom's and Lick's model. The correlation between parameters in resuspension/sedimentation models can be very high, leading to an uncertainty in parameter estimates of 25-50. The parameter estimations based on the flume data are up to orders of magnitude higher than those estimated from field observations.

scholarly journals Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data

2020 ◽  
Vol 37 (7) ◽  
pp. 2124-2136
Author(s):  
Paul D Blischak ◽  
Michael S Barker ◽  
Ryan N Gutenkunst
Keyword(s):  
Puma Concolor ◽  
Demographic History ◽  
Random Mating ◽  
Simulated Data ◽  
Conservation Agriculture ◽  
Model Fit ◽  
Parameter Estimates ◽  
History Of ◽  
Demographic Inference ◽  
Inbred Populations

Abstract Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model’s ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F = 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.

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