International Day Of Women in Statistics and Data Science

"Given the increasing threat of global warming, it is important to understand not only
global weather patterns but also regional variations throughout countries. Functional
Linear Mixed-effects Model (FLMM), an emerging statistical tool, provides a comprehensive
framework for analyzing functional data (e.g., data viewed as a function
or curve) with repeated observations, allowing researchers to identify patterns and relationships
in the data. This study applies the functional linear mixed-effects model
(FLMM) to recognize the effects of temporal and regional variations of short-time
weather projection (monthly precipitation on daily temperature). We deployed FLMM
using the daily temperature and monthly precipitation of nine weather stations(Dhaka,
Chattogram (Patenga), Chattogram (Ambagan), Rajshahi, Khulna, Barisal, Sylhet,
Rangpur and Mymensingh) of Bangladesh where each station shares the common
population effects with their individual scalar covariate effects along with same slope
functions. To estimate variance parameters and fixed-effects and random effects, the
REML-based EM algorithm proposed by has been applied. Empirical
results show significant differences in the effects of monthly precipitation on daily
temperature among regions of Bangladesh. We anticipated that FLMM is an emerging
model that can reveal the effects of monthly precipitation and the pace of daily
temperature fluctuations over time."