Search results
Results From The WOW.Com Content Network
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as in parametric statistics. [1] Nonparametric statistics can be used for descriptive statistics or statistical ...
Parametric statistics is a branch of statistics which leverages models based on a fixed (finite) set of parameters. [1] Conversely nonparametric statistics does not assume explicit (finite-parametric) mathematical forms for distributions when modeling data. However, it may make some assumptions about that distribution, such as continuity or ...
a model is "non-parametric" if all the parameters are in infinite-dimensional parameter spaces; a "semi-parametric" model contains finite-dimensional parameters of interest and infinite-dimensional nuisance parameters; a "semi-nonparametric" model has both finite-dimensional and infinite-dimensional unknown parameters of interest.
The data from a study can also be analyzed to consider secondary hypotheses inspired by the initial results, or to suggest new studies. A secondary analysis of the data from a planned study uses tools from data analysis, and the process of doing this is mathematical statistics. Data analysis is divided into:
There are two approaches to statistical inference: model-based inference and design-based inference. [2] [3] [4] Both approaches rely on some statistical model to represent the data-generating process. In the model-based approach, the model is taken to be initially unknown, and one of the goals is to select an appropriate model for inference ...
The in-depth analysis of a small purposive sample or case study enables the discovery and identification of patterns and causal mechanisms that do not draw time and context-free assumptions. Another advantage of nonprobability sampling is its lower cost compared to probability sampling.
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. That is, no parametric equation is assumed for the relationship between predictors and dependent variable.
Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density = where is or : Start with N {\displaystyle N} points θ 1 , … , θ N {\displaystyle \theta _{1},\ldots ,\theta _{N}} sampled from prior.