Introduction to Joint Generalized

The current article aims to derive teenage induced abortions trends based on statistical approach Joint Generalized Linear Models (JGLMs), which are very little studied in the women’s health literature.

A joint generalized estimating equation (GEE) method is developed for this purpose and the resulting correlation coefficients are shown to satisfy the constraints.

In this paper, we proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model.

Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM).

Based on the excellent control performance and robustness presented by Generalized Predictive Control when dealing with multivariable systems, rudder/fin stabilisation roll of the rudder blade joint generalized predictive controller is designed while taking the rudder angle and fin angle constraints into full consideration, and system simulation research was carried out in different sea states.

It focuses on the Joint Generalized Linear Model (Joint-GLM) instead of the traditional Generalized Linear Model (GLM).

In this paper, we propose the three sub-optimal precoded single-mode joint generalized spatial modulation (SM-JGSM) constellation designs for high correlated indoor multi-input multi-output visible light communication (MIMO-VLC) channels.

To this, we propose a label guided correlation cross-modal hashing method (LGCH), which investigates an alternative way to exploit label information for effective cross-modal retrieval from two aspects: 1) LGCH learns the discriminative common latent representation across modalities through joint generalized canonical correlation analysis (GCCA) and a linear classifier; 2) to simultaneously generate binary codes and hashing function, LGCH introduces an adaptive parameter to effectively fuse the common latent representation and the label guided representation for effective cross-modal retrieval.