Latent Variable : A latent variable is a hidden or unobserved construct that is believed to explain the relationship between observed variables. These latent variables are not directly measured, but rather inferred from the observed variables. For example, let’s consider the relationship between a person’s height and their weight. It is well-known that taller individuals […]

Latent root distributions : A latent root distribution is a mathematical concept that describes the distribution of certain values within a system. These values, known as latent roots, are typically hidden or unobservable, and their distribution can provide valuable information about the underlying structure of the system. One example of a latent root distribution can […]

Latent period : The latent period is the time between the initial exposure to a pathogen and the appearance of symptoms of the disease that the pathogen causes. The length of the latent period can vary depending on the type of pathogen and the individual’s immune system. For example, the latent period for the common […]

Latent class identifiability display : Latent class identifiability is a statistical concept that refers to the ability to identify and distinguish different subgroups or classes within a population based on observed characteristics or variables. This is often used in social and behavioral sciences to study complex phenomena such as personality traits, attitudes, and behaviors. To […]

Latent class analysis : Latent class analysis is a statistical technique used to identify distinct groups within a population based on their responses to a set of observed variables. These groups, known as latent classes, are hidden or unobserved and can only be inferred from the data. One example of latent class analysis is in […]

Lasso : Lasso is a type of regression analysis that uses shrinkage and variable selection to improve the accuracy and interpretability of the model. It is a useful tool for data analysts and researchers who want to identify the most important predictors in a large dataset, or who want to avoid overfitting in their models. […]

Large Sample Methods : Large sample methods are statistical techniques used to analyze data sets with a large number of observations. These methods are useful for making inferences about a population based on the characteristics of a sample. There are two main types of large sample methods: hypothesis testing and estimation. Hypothesis testing is a […]

Laplace approximation : Laplace approximation is a method used in statistics and machine learning to approximate a complex posterior distribution with a simpler, Gaussian distribution. This allows for easier calculation of probabilities and inference, as well as more efficient optimization of model parameters. One example of Laplace approximation is in Bayesian linear regression, where the […]

Landmark-based shape analysis : Landmark-based shape analysis is a method used in the field of biology to study the shape and form of biological structures, such as organs or bones. This method involves identifying specific points on the structure, known as landmarks, and using mathematical and statistical techniques to analyze the distances and angles between […]

Landmark analysis : Landmark analysis is a technique used in geography and geology to study the spatial relationships between different features on the earth’s surface. The technique involves identifying key landmarks, such as mountains, rivers, or other natural features, and using them as reference points to measure the distances and directions between other features. This […]