Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Joint maximum likelihood estimation"”.
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Penalized Joint Maximum Likelihood Estimation Applied to Two Parameter Logistic Item Response Models
… yield meaningless results. Recently, penalized estimation methods have been developed to analyze data sets that may include more variables than observations. The main focus of this study was to apply LASSO and ridge regression penalization techniques to IRT models in order to better estimate …
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DIMTEST Enhancements and Some Parametric IRT Asymptotics
The joint consistency of item and ability parameter estimation remains a challenging problem in IRT parametric modeling. Although many simulation studies have been conducted on the item and ability parameter estimates obtained by joint maximum likelihood estimation which is implemented in LOGIST …
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Methods and Theory for Joint Estimation of Incidental and Structural Parameters in Latent Class Models
Marginal maximum likelihood estimation has become the standard for parameter estimation in latent variable models. However, there are instances when alternative estimators that jointly estimate incidental parameters and structural parameters might be easier to implement. A drawback to joint …
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Experimental and self-reported measures of impulsivity: A reconsideration
… Impulsiveness Scale-11 (BIS-11). We conduct joint maximum likelihood estimation to estimate discounting and risk preference parameters, and compare them to the BIS-11 total score and subscales. Our results show that the BIS-11 is related to time preferences, while only the motor impulsiveness …
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Dual Model Robust Regression
… in the mean and variance models through joint maximum likelihood. Estimation of the mean and variance parameters are interrelatedas the responses in the variance model are the squared residuals from the fit to the means model. When one or both of the models (the mean or variance model) …
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Least squares estimation for latent variables with dichotomous item response data
… this study. A brief review of existing parameter estimation methods suggested the utility of a parameter estimation method which requires no specific assumption. By re-examining the performance of maximizing the ln $L$ of the joint maximum likelihood estimation (JMLE; Lord, 1980), and by applying …
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Hurdles and solutions for cognitive diagnosis
… to provide the solutions to these problems: the estimation of the Q-Matrix, the estimation of model parameters, and the assessment of model-fit.