Application of Advanced Statistical Models in Big Data Analysis: Modern Methodologies and Techniques

This examine investigates the software of advanced statistical fashions in huge records analytics, that specialize in their capability to cope with demanding situations in accuracy, scalability, and moral alignment inside records-driven choice-making.
The research employs a multi-faceted method, integrating Gradient Boosting Machines (GBM) with hyperparameter tuning for credit chance prediction, Bayesian Reinforcement Learning (BRL) for dynamic uncertainty modeling, and quantum computing simulations for optimization responsibilities. Distributed computing frameworks, including Kubernetes, and privacy-retaining techniques like homomorphic encryption are evaluated to decorate computational and ethical robustness. The GBM version carried out a 20% discount in category blunders in comparison to conventional methods, whilst BRL proven superior interpretability in stochastic environments. Real-time adaptive fashions reduced latency through 60% in streaming facts situations, and quantum-greater algorithms showed a 75% development in dimensionality reduction performance. Ethical frameworks, such as adverse debiasing, decreased demographic parity gaps from 15% to three% without compromising model overall performance. The findings recommend for hybrid models that merge statistical intensity with computational innovation, emphasizing their essential position in overcoming scalability and bias challenges. Future studies must prioritize quantum-geared up architectures and interdisciplinary methodologies to maintain improvements in big records analytics. This work contributes a foundational framework for deploying statistically rigorous, ethically aligned, and computationally efficient solutions in complex facts ecosystems.