Long memory processes : Long memory processes, also known as long-range dependence or persistence, refer to the tendency for a time series to exhibit significant autocorrelation over long periods of time. This means that the current value of a time series is heavily influenced by its past values, even those from far back in time. […]

Longitudinal Data : Longitudinal data is a type of data that is collected over a period of time, often involving multiple observations of the same individuals or units. This type of data allows researchers to track changes or trends within a population over time, providing valuable insights into the dynamics of a given phenomenon. One […]

Long short-term memory (LSTM) : Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture specifically designed to address the vanishing gradient problem in RNNs. This problem occurs when the network is unable to remember information from long sequences due to the decreasing influence of previous input over time, leading to poor […]

Lomb periodogram : The Lomb periodogram is a type of spectral estimation method used to identify significant periodic signals in a time series data set. It is based on the concept of least-squares fitting of sinusoidal functions to the data, and is commonly used in fields such as astronomy, geophysics, and signal processing. One of […]

Logrank test : The logrank test is a statistical test used to compare the survival rates of two or more groups. This test is commonly used in medical research to evaluate the effectiveness of a new treatment or intervention on a particular condition or disease. For example, a study may be conducted to evaluate the […]

Log-linear models : A log-linear model is a statistical method used to analyze and understand the relationship between multiple categorical variables. It is a type of regression analysis that is used to model the relationship between two or more variables, where the dependent variable is a logarithmic transformation of the independent variable. One example of […]

Logistic Regression : Logistic regression is a type of statistical analysis used to predict the outcome of a binary event, such as whether a student will pass or fail a test, or whether a customer will churn or not. It is a powerful tool that is commonly used in fields such as healthcare, finance, and […]

Log Loss : Log loss, also known as cross entropy loss, is a performance metric used in classification tasks. It measures the difference between the predicted probability and the actual outcome. For example, in a binary classification task where the classes are “positive” and “negative”, the predicted probabilities are calculated for each class. If the […]

Logarithmic transformation : A logarithmic transformation is a way of changing the scale of an axis on a graph. This can be useful when the data being plotted covers a very wide range of values, making it difficult to see patterns or trends. By using a logarithmic scale, the data can be spread out more […]

Locally weighted regression : Locally weighted regression (LWR) is a machine learning technique that allows for non-linear regression models to be fit to data. It does this by weighting the data points in a given region differently, allowing for more flexibility in the model. One example of LWR in action is in predicting housing prices. […]