Expectation Maximization (EM) & implementation
Expectation Maximization (EM) is an iterative algorithm used for finding maximum likelihood estimates of parameters in statistical models, particularly when…
Category: data preprocessing
A comic guide to denoising noisy data
Handling noisy data is a crucial step in data preprocessing and analysis. In general, here are some common approaches to…
A comical guide to Missing Not At Random (MNAR)
Recall that Missing Not At Random (MNAR) is a type of missing data mechanism where the probability of missingness is…
Generating missing data and evaluating missing data analysis in Python
Generating missing values Generating missing values with a given percentage of missingness for a dataframe or numpy array: Generating missing…
Introduction to Principal Component Analysis (PCA) and implementation in R and Python
Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction, which simplifies the complexity in high-dimensional data while…
denoising via dimension reduction in python
Dimension reduction methods like Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) can be used for denoising data because…
why we can & probably should use missing at random imputation methods for data that’s not missing at random?
Missing At Random (MAR) imputation methods are based on the assumption that the chance of missing data is not related…
Missing data analysis: where’s your missing piece?
Why missing data occurs can be attributed to various reasons, including human error, malfunctioning equipment, or even intentional omission. It…
Imputation using SoftImpute in python
SoftImpute is a matrix completion algorithm in Python that allows you to fill in missing data in your dataset. This…
Multiple Imputation with Chained Equations method & Python codes
MICE (Multiple Imputation by Chained Equations) is a statistical method used for handling missing data by creating multiple imputations or…