Complex traits with multiple phenotypic values changing over time are called time-dependent or longitudinal traits. Knowledge of the genetic effects influencing longitudinal patterns is important to ...
Greenland (2000, Biometrics 56, 915-921) describes the use of random coefficient regression to adjust for residual confounding in a particular setting. We examine this setting further, giving ...
On the Estimation of Structural Change: A Generalization of the Random Coefficients Regression Model
Balvir Singh, A. L. Nagar, N. K. Choudhry and Baldev Raj The International Economic Review was established in 1960 by two of the most active and acclaimed scholars in the economics profession: Michio ...
The slope and intercepts we compute in a regression model are statistics calculated from the sample data. They are point estimates of corresponding parameters; namely, the slope and intercept in the ...
Alcacer, Juan, Wilbur Chung, Ashton Hawk, and Goncalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific ...
Random Coefficient Modelling of the Global Effect of Exchange and Monetary Policy Rates on Inflation
This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian Generalized Additive Mixed Models (GAMMs). Extending an ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...
Ordinary regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the ordinary regression ...
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