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1	Parameter Estimation in Linear-Linear Segmented Regression Hernandez, Erika Lyn 20 April 2010 (has links) (PDF) Segmented regression is a type of nonlinear regression that allows differing functional forms to be fit over different ranges of the explanatory variable. This paper considers the simple segmented regression case of two linear segments that are constrained to meet, often called the linear-linear model. Parameter estimation in the case where the joinpoint between the regimes is unknown can be tricky. Using a simulation study, four estimators for the parameters of the linear-linear model are evaluated. The bias and mean squared error of the estimators are considered under differing parameter combinations and sample sizes. Parameters estimated in the model are the location of the change-point, the slope and intercept of the first segment, the change in slope from the first segment to the second, and the variance over both segments. change-point regression broken-stick model maximum likelihood Bayesian estimators bent-cable regression Statistics and Probability
2	A Monte Carlo Study of Parallel Analysis, Minimum Average Partial, Indicator Function, and Modified Average Roots for Determining the Number of Dimensions with Binary Variables in Test Data: Impact of Sample Size and Factor Structure Ruengvirayudh, Pornchanok 12 July 2018 (has links) No description available. Educational Evaluation parallel analysis randomly generated data threshold type of correlation matrices source of eigenvalues minimum average partial broken stick average root modified average roots imbedded error indicator function EFA sample size factor structure

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Parameter Estimation in Linear-Linear Segmented Regression

A Monte Carlo Study of Parallel Analysis, Minimum Average Partial, Indicator Function, and Modified Average Roots for Determining the Number of Dimensions with Binary Variables in Test Data: Impact of Sample Size and Factor Structure