Parametric statisticsParametric statistics is a branch of statistics that is concerned with the analysis of and inference from data assuming that the underlying distribution, from which the observed data was drawn, can be described by a finite set of (unknown) parameters. In contrast, nonparametric statistics does not assume explicit (finite-parametric) mathematical forms for distributions when modeling data.
Robust statisticsRobust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers.
Nonparametric regressionNonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information derived from the data. That is, no parametric equation is assumed for the relationship between predictors and dependent variable.
Statistical inferenceStatistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics.
Probability distributionMathematical function for the probability a given outcome occurs in an experimentIn probability theory and statistics, a probability distribution describes how probabilities are assigned to the possible results of a random phenomenon—more precisely, to events, which are sets of possible outcomes of a probabilistic experiment. Informally, a probability distribution tells us how likely different results are.
K-nearest neighbors algorithmIn statistics and machine learning, the k-nearest neighbors algorithm (k-NN) is a non-parametricsupervised learning method that assigns weightage only to the k (number of) nearest neighbors of an entity in making a decision about the entity. It is used both in classification -- where a new example is assigned a label based on the labels of its k nearest training examples; and in regression -- where the prediction is computed from the values of those neighbors.Descriptive statisticsA descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics.
Support vector machineIn machine learning, a support vector machine (SVM) or support vector network is a supervisedmax-margin model with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
Kernel density estimationIn statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made based on a finite data sample.
ParameterA parameter (from Ancient Greek (pará)'beside, subsidiary' and (métron)'measure'), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.).RankingA ranking is a relationship between a set of items, often recorded in a list, such that, for any two items, the first is either "ranked higher than", "ranked lower than", or "ranked equal to" the second. In mathematics, this is known as a weak order or total preorder of objects. It is not necessarily a total order of objects because two different objects can have the same ranking. The rankings themselves are totally ordered.Distribution (mathematical analysis)In mathematical analysis, distributions are a type of generalized function that makes it possible to differentiate functions whose derivatives do not exist in the classical sense. In particular, any locally integrable function has a distributional derivative.*As an Amazon Associate I earn from qualifying purchases.