(47) Here V is the covariance of the vector of independent Poisson observations Y, and Z are the pseudo-observations. V and Z depend on . The covariance of Z conditional on and  is VϪ1. Initialize ˆ ϭ ˆ0, where the components of a are ln(Y) and b ...
A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance ...
estimating the prevalence of the disease is also a challenging task. Large-scale testing of a population where a fraction of individuals is infected, relies on unbiased sampling, reliable tests, and accurate recording of results. One of the main sources of systematic bias arises from the tested ...
To overcome the issues, MacKinnon and White [60] employed the het- eroscedastic-consistent covariance matrix (HCCM) esti- mators, such as HC0, HC1, HC2 and HC3 that provide a consistent estimator of the covariance matrix. Lately, Davidson and MacKinnon [61] suggest three (HC0, HC2, ...
The multivariate analysis of covariance (MANCOVA) showed that the Aβ+ group performed worse in tests related to the verbal and visual delayed recall, semantic verbal fluency, and inhibition of cognitive inference within the three cognitive domains. The Preclinical Amyloid Sensitive Composite (PASC) ...
To analyze the interrelationships between the average 24-h Tb and covariates Ta, day length, and snow depth, the analysis of covariance (ANCOVA) was performed with the linear mixed model analysis. The model included the individual as a random factor and the covariates Ta, day length, snow ...
Standard deviations of estimated parameters were calculated from Jacobian covariance matrix. For control the standard deviations were computed by bootstrap method. Data and fits were visualized by Matplotlib package. Analysis of experimental data. Data from experiments were processed in Libre Office ...
The dynamic two-step system GMM estimator is asymptotically more efficient than the first-step estimator because it uses the residuals derived from the first step to ensure consistency in the estimation of the variance-covariance matrix, thereby relaxing the independence and homoscedasticity assumptions ...
Recently, nonlinear mixed-effects (NLME) model15,17for repeated measures dose-response data have become popular due to their flexible covariance structure which allows for the joint modeling of multiplein vitromeasurements taken off a set of CCLs. An advantage of using the NLME model is that both...
“Covariance Profiling for an Adaptive Kalman Filter to Suppress Sensor Quantization Effects,” 43rd IEEE Conference on Decision and Control, vol. 3, Dec. 14-17, 2004, pp. 2680-2685. Girod et al., “The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform,”...