Non-parametric analysis of covariance : Nonparametric analysis of covariance (ANCOVA) is a statistical technique used to analyze the relationship between two or more continuous variables while controlling for the effects of one or more categorical variables. Unlike traditional parametric ANCOVA, nonparametric ANCOVA does not make assumptions about the underlying distribution of the data and is […]

Non-parametric maximum likelihood(NPML) : Nonparametric maximum likelihood (NPML) is a statistical method that is used to estimate the parameters of a model without making any assumptions about the underlying distribution of the data. This method is often used when the data is complex or when the underlying distribution is unknown or difficult to model. An […]

Non-parametric Bayesian models : Nonparametric Bayesian models are a type of statistical model that do not make assumptions about the form or shape of the underlying data distribution. This is in contrast to parametric models, which assume a specific functional form for the data distribution (e.g. normal distribution, Poisson distribution, etc.). Nonparametric models allow for […]

Non-negative garrotte : Nonnegative garrotte is a mathematical concept that refers to a type of optimization problem in which the variables being optimized must be nonnegative (greater than or equal to zero). This means that the solution to the problem cannot involve negative values for any of the variables. One common example of a nonnegative […]

Non-metric scaling : Nonmetric scaling is a type of data analysis technique used to identify patterns and relationships within a dataset. It is commonly used in the field of psychology, where researchers may want to understand how different variables are related to one another. Nonmetric scaling can be contrasted with metric scaling, which involves measuring […]

Nonlinear model : Nonlinear models are mathematical models that exhibit nonlinear relationships between the input variables and the output variables. These models are used to describe complex systems that cannot be accurately represented using linear models. In contrast to linear models, nonlinear models have non-constant coefficients and cannot be represented using a straight line on […]

Nonlinear mapping (NLM) : Nonlinear mapping (NLM) is a mathematical technique that involves the use of nonlinear functions to transform input data into output data. This technique is often used in machine learning, image processing, and other areas where the relationships between input and output data are complex and not easily represented by linear functions. […]

Non-informative prior distribution : A non-informative prior distribution is a type of probability distribution used in Bayesian statistics that does not contain any information about the likelihood of certain events occurring. This type of distribution is typically used when there is little or no prior knowledge about the likelihood of certain outcomes, or when it […]

Non-informative censoring : Non-informative censoring occurs when data is collected over a certain period of time and some observations are not available past a certain point. This can occur for a variety of reasons, such as the end of a study or the individual no longer being available for follow-up. Non-informative censoring does not provide […]

Non-Gaussian time series : Non-Gaussian time series refers to time series data that does not follow a normal or Gaussian distribution. A Gaussian distribution is characterized by a bell-shaped curve, with most data points occurring around the mean, and a symmetrical distribution on either side of the mean. Non-Gaussian time series, on the other hand, […]