Statistical Modeling

Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance ArXiv ID: 2406.01335 “View on arXiv” Authors: Unknown Abstract Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge. ...

A K-means Algorithm for Financial Market Risk Forecasting ArXiv ID: 2405.13076 “View on arXiv” Authors: Unknown Abstract Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today’s society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate ...

Copulas forFinance- A Reading Guide and Some Applications ArXiv ID: ssrn-1032533 “View on arXiv” Authors: Unknown Abstract Copulas are a general tool to construct multivariate distributions and to investigate dependence structure between random variables. However, the concept of cop Keywords: Copulas, Multivariate Distributions, Dependence Structure, Random Variables, Statistical Modeling, Quantitative Methods Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper focuses on theoretical copula constructions and dependence structures with advanced mathematics, but lacks implementation details, backtests, or empirical data. flowchart TD A["Research Goal:<br>Review Copulas for Finance"] --> B["Key Methodology:<br>Literature Review & Analysis"] B --> C["Data/Input:<br>Financial Return Datasets<br>and Models"] C --> D["Computational Process:<br>Model Fitting &<br>Dependence Estimation"] D --> E["Key Outcomes:<br>Capturing Non-Linear Dependence<br>and Risk Assessment"]