Introducing ikNN: An Interpretable k Nearest Neighbors Model
Introducing ikNN: An Interpretable k Nearest Neighbors Model
Recent posts
Sequential Detection of Switches in Models with Changing Structures
Sequential detection of structural changes in models is a critical aspect in various domains, enabling timely and informed decision-making. This involves identifying moments when the parameters or structure of a model change, often signaling significant events or shifts in the underlying data-gen...
Frequent Patterns Outlier Factor
Outlier detection is a critical task in machine learning, particularly within unsupervised learning, where data labels are absent. The goal is to identify items in a dataset that deviate significantly from the norm. This technique is essential across numerous domains, including fraud detection, s...
Central Limit Theorem for m-dependent Random Variables Under Sub-linear Expectations
This article rigorously explores the Central Limit Theorem for m-dependent random variables under sub-linear expectations, presenting new inequalities, proof outlines, and implications in modeling dependent sequences.
Detecting Outliers Using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a robust technique used for dimensionality reduction while retaining critical information in datasets. Its sensitivity makes it particularly useful for detecting outliers in multivariate datasets. Detecting outliers can provide early warnings of abnormal cond...
Interpretable Outlier Detection with Counts Outlier Detector (COD)
Overview of the Counts Outliers Detector (COD)
Applying Einstein’s Principle of Simplicity Across Disciplines
Albert Einstein’s quote, “Everything should be made as simple as possible, but not simpler,” encapsulates a fundamental principle in science and analytics. It emphasizes the importance of simplicity and clarity while cautioning against oversimplification that can lead to loss of essential detail ...
Testing and Evaluating Outlier Detectors Using Doping
Outlier detection presents significant challenges, particularly in evaluating the effectiveness of outlier detection algorithms. Traditional methods of evaluation, such as those used in predictive modeling, are often inapplicable due to the lack of labeled data. This article introduces a method k...
Copula, GARCH, and Other Financial Models
An in-depth look at financial models such as Copula and GARCH, their importance in quantitative analysis, and practical applications with Python.
Understanding Uncertainty in Statistical Estimates: Confidence and Prediction Intervals
Statistical estimates always have some uncertainty. Consider a simple example of modeling house prices based solely on their area using linear regression. A prediction from this model wouldn’t reveal the exact value of a house based on its area, because different houses of the same size can have ...