Mulati Tuerde

1. Introduction Latent variable models are designed to estimate the relationships between observed variables and a hypothesized latent construct that is presumed to predict these variables. Dynamic latent variable models (DLVMs) extend this concept by accounting for unobserved heterogeneity through the explicit modeling of dependencies between observations and latent variables.

3.1. Simulation Study In this paper, we use randomly generated precipitation data with a time length of 1380 as the original data. If the daily precipitation is sorted from largest to smallest, the range of the first six precipitation amounts is [48.19, 101.15] in mm, and the range of the remaining precipitation amounts is within 26.64 mm.

1. Introduction Koenker and Bassett [1] introduced the concept of the quantile regression (QR) model. Unlike the linear regression (OLS) model, OLS focuses solely on modeling the mean of the response variable. QR allows us to estimate regression coefficients at different quartiles of the response variable. This enables us to capture the varying degrees of correlation between the covariates and the response variable.