Applied Linear Regression.

From the Publisher:
Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control.

Kalman Filtering and Neural Networks

https://www.statmodel.com/download/Article_0091.pdf

Latent variable structural equation modeling with categorical data

https://www.princeton.edu/~mwatson/papers/Engle_Watson_JoE_1983.pdf

Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models

The Structure of Inference.

Many score statistics used for testing trends in means, either based on the observed values of the response variables or their ranks, can be expressed in terms of quadratic forms. This article explores systematically the robustness of optimality of these statistics, as well as the optimal properties of the associated tests.

https://www.jstor.org/stable/pdf/2287123.pdf

Score Tests for Regression Models

This article considers both likelihood and Bayesian estimation procedures for a model with multiple indicators and multiple causes of a single unobservable latent variable. The model is complicated by its reduced form, which displays a mixture of econometric and psychometric themes. We avoid this complexity via the estimation of the unobservable latent variable through direct use of the original structural form. This approach not only provides maximum likelihood estimates that are equivalent to those derived from the reduced form, but it also permits a feasible Bayesian approach, which would be technically complex to execute otherwise.

Chan-Fu Chen

Papers

Score Tests for Regression Models

The EM Approach to the Multiple Indicators and Multiple Causes Model via the Estimation of the Latent Variable