Tag Archives: Non-Parametric

XLSTAT 2018.2

Type: Demo
Cost: $295
Size: 140.60 MB
Release date: Mar 19 2018
Platform: Win2000,WinXP,Win7 x32,Win7 x64,Windows 8,Windows 10,WinServer,WinOther,WinVista,WinVista x64
Publisher’s Site:  https://www.xlstat.com
Publisher’s Product Page:  https://www.xlstat.com/en/solutions
Country: France


XLSTAT

XLSTAT is a complete analysis and statistics add-in for Excel. It has been developed since 1993. XLSTAT includes more than 200 features in general or field-specific solutions. The use of Excel as an interface makes XLSTAT a user-friendly and highly efficient statistical and multivariate data analysis package.
It includes regression (linear, logistic, nonlinear), multivariate data analysis (Principal Component Analysis, Discriminant Analysis, Correspondence Analysis, Multidimensional Scaling, Agglomerative Hierarchical Clustering, K-means, K-Nearest Neighbors, Decision trees), correlation tests, parametric tests, non parametric tests, ANOVA, ANCOVA, mixed models and much more. Field-specific solutions allow for advanced multivariate analysis (RDA, CCA, MFA), Preference Mapping and other sensometrics tools, Statistical Process Control, Simulations, Time series analysis, Dose response effects, Survival models, Conjoint analysis, PLS modelling, Structural Equation Modelling, OMICS data analysis… Optional modules include 3D Visualization and Latent Class models.
The XLSTAT statistical analysis software is compatible with all Excel versions from 2003 to 2016. A Mac version is also available on the XLSTAT website, and works on Excel 2011 & 2016.

KNN-WG 1.0

Type: Commercial
Cost: $23.95
Size: 56.30 MB
Release date: Jan 01 2017
Platform: Win2000,WinXP,Win7 x32,Win7 x64,Windows 8,Windows 10,WinServer,WinOther,WinVista,WinVista x64
Publisher’s Site:  http://www.agrimetsoft.com/
Publisher’s Product Page:  http://www.agrimetsoft.com/HelpKnn.aspx
Country: United States of America


KNN-WG

The K-nearest neighbors (K-NN) is an analogous approach. This method has its origin as a non-parametric statistical pattern recognition procedure to distinguish between different patterns according to a selection criterion. Through this method, researchers can generate future data. In other words, the KNN is a technique that conditionally resamples the values from the observed record based on the conditional relationship specied. The KNN is most simple approach.

The most promising non-parametric technique for generating weather data is the K-nearest neighbor (K-NN) resampling approach. The K-NN method is based on recognizing a similar pattern of target le within the historical observed weather data which could be used as reduction of the target year (Young, 1994; Yates, 2003; Eum et al., 2010). The target year is the initial seed of data which, together with the historical data, are required as

input les for running the model. This method relies on the assumption that the actual weather data observed during the target year could be a replication of weather recorded in the past. The k-NN technique does not use any predened mathematical functions to estimate a target variable.

Actually, the algorithm of this method typically involves selecting a specied number of days similar in characteristics to the day of interest. One of these days is randomly resampled to represent the weather of the next day in the simulation period. The nearest neighbor approach involves simultaneous sampling of the weather variables, such as precipitation and temperature. The sampling is carried out from the observed data, with replacement.

The K-NN method is widely used in agriculture (Bannayan and Hoogenboom, 2009), forestry (Lopez et al., 2001) and hydrology (Clark et al., 2004; Yates et al., 2003).