Statistics

Statistics

The Intersection of Machine Learning and Tail Risk Management in HFT

Introduction High-Frequency Trading (HFT) has transformed the financial markets, bringing increased liquidity and faster transaction times. However, it has also brought with it new and complex risks, particularly in the area of tail risk. Tail risk refers to the risk of rare and unexpected events that can have a significant impact on the market. In …

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Exploring the Effectiveness of Imbalanced Data Correction Methods in Mixed Linear Regression Models

Introduction In recent years, the amount of data collected in various fields has grown rapidly, and machine learning algorithms have become increasingly popular for analyzing such data. However, a common issue faced when working with large datasets is class imbalance, where one class in the target variable is greatly outnumbered by the other. This imbalance …

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From Outliers to Inliers: Robust Non-Parametric Regression with Median-of-Means

Regression analysis is a widely used statistical tool for predicting a continuous dependent variable based on one or more independent variables. However, traditional regression methods, such as linear and polynomial regression, can be sensitive to outliers and make incorrect predictions if the assumptions of normality and homoscedasticity are violated. To address these limitations, researchers have …

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Optimizing the Accuracy of Time Series Predictions: An Introduction to the Forward-Backward Algorithm

Introduction In today’s fast-paced world, businesses, industries, and organizations rely heavily on data-driven decision making. The ability to predict future trends and patterns in data can be incredibly valuable for forecasting and planning. One of the most important areas of data analysis is time series analysis, which involves studying and understanding sequential data over time. …

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Dynamic Factor Analysis vs Traditional Methods: Comparing the Accuracy of Inflation Forecasting

Introduction Inflation forecasting is a crucial task for policymakers and central banks as it helps in determining the appropriate monetary policy. The accuracy of inflation forecasting is of paramount importance, as it directly impacts the economy. In this blog post, we will compare the accuracy of dynamic factor analysis (DFA) and traditional methods in forecasting …

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Multiple Hypothesis Testing: How to Balance Power and False Positive Rate

Introduction In the field of statistical analysis, multiple hypothesis testing is a common problem that arises when a researcher conducts multiple experiments or tests simultaneously. The problem arises because the more hypotheses that are tested, the higher the probability of obtaining a false positive result. In this blog post, we will discuss the concept of …

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Beyond the Likert Scale: Innovative Approaches to Measuring Attitudes and Opinions

Introduction: The Likert scale is a popular tool used in survey research to measure attitudes, opinions, and perceptions. However, the Likert scale has its limitations and may not always be the best tool for measuring attitudes and opinions. In this blog post, we will explore some innovative approaches to measuring attitudes and opinions that go …

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Multiple Imputation for Propensity Score Analysis with Covariates Missing at Random

Introduction Missing data is a common problem in statistical analysis, and can lead to biased or inefficient estimates if not handled properly. One method for dealing with missing data is multiple imputation, which involves creating multiple plausible values for the missing data and analyzing each imputed dataset separately, before combining the results. In this blog …

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The Power of Bayesian Methods in Housing Investment Forecasting: A Look at BVARS and FAVARS

In recent years, Bayesian methods have gained popularity in the field of housing investment forecasting. These methods, which are based on Bayes’ theorem, allow for the incorporation of prior knowledge and uncertain parameters in the forecasting process. Two specific methods that have been widely used in this context are Bayesian Vector Autoregression (BVAR) and Factor-augmented …

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Penalized Two-Pass Regression: A Step-by-Step Guide

Introduction Penalized regression is a technique used in machine learning and statistics to improve the performance of linear regression models. One specific variation of penalized regression is known as two-pass regression, which involves two stages of variable selection and regularization. In this blog post, we will discuss the concept of penalized two-pass regression, its advantages, …

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