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Análisis de Nelson-Aalen
What is the Nelson-Aalen analysis The Nelson-Aalen analysis method belongs to the descriptive methods for survival analysis such as life table analysis and Kaplan-Meier analysis. The Nelson-Aalen approach can quickly give you a curve of cumulative hazard and estimate the hazard functions based on irregular time intervals. Nelson-Aalen analysis is used to analyze how a given population evolves with...
Análisis espectral
Spectral analysis Spectral analysis allows transforming a time series into its coordinates in the space of frequencies, and then to analyze its characteristics in this space. The magnitude and phase can be extracted from the coordinates. It is then possible to build representations such as the periodogram or the spectral density, and to test if the series is stationary. By studying the spectral density,...
Diseños para el análisis conjunto
Why do we use design of experiments for conjoint analysis The principle of conjoint analysis is to present a set of products (also known as profiles) to the individuals who will rank, rate, or choose some of them. In an ideal analysis, individuals should test all possible products. But it is soon impossible; the capacity of each being limited and the number of combinations increases very rapidly with...
Análisis MaxDiff
What is MaxDiff analysis MaxDiff or Maximum Difference Scaling is a method introduced by Jordan Louvière that allows to obtain importance of attributes. Attributes are presented to a respondent and he has to choose the best and worst attributes (most important / least important) Two steps are needed to apply that method. First, a design has to be generated so that each attribute is presented with...
MONANOVA - Regresión Monotónica
The MONANOVA model definition Monotone regression and the MONANOVA model differ only in the fact that the explanatory variables are either quantitative or qualitative. These methods are based on iterative algorithms based on the ALS (alternating least squares) algorithm. Their principle is simple, it consists of alternating between a conventional estimation using linear regression or ANOVA and a monotonic...
CATATIS
What is CATATIS method and when to use it? The CATATIS method is an improvement of the usual method for processing CATA data. It is considered as the equivalent of the STATIS method for this type of data. The great interest of CATATIS lies in the fact that atypical assessors have a lower weight than those who agree with the rest of the panel. Therefore, the analysis best reflects the general point...
Simulación para el análisis conjunto
Principle of simulation for conjoint analysis Conjoint analysis is a comprehensive method for the analysis of new products in a competitive environment. Once the analysis has been performed, the major advantage of conjoint analysis is its ability to perform market simulations using the obtained utilities. The products included in the market do not have to be part of the tested products. Outputs from...
Coordenadas Paralelas
Use this tool to visualize multidimensional data (described by P numerical and Q nominal variables) on a single two dimensional chart. This visualization method is useful for data analysis when you need to discover or validate a groups structure, for example after a clustering. Using this method you are able to visually determine which variables are discriminant.
Análisis descriptivo de series temporales
One of the key issues in time series analysis is to determine whether the value we observe at time t depends on what has been observed in the past or not. If the answer is yes, then the next question is how. Autocovariances, autocorrelations, and partial autocorrelations The sample autocovariance function (ACVF) and the autocorrelation function (ACF) give an idea of the degree of dependence between...
Análisis de correlaciones canónicas generalizadas regularizadas (RGCCA)
What is Regularized Generalized Canonical Correlation Analysis (RGCCA)? RGCCA is a method introduced by Tenenhaus et al. (2011). It allows to optimize a global function using an algorithm very similar to the PLSPM algorithm. Unlike the PLS approach, the results of the RGCCA are correlations between latent variables and between manifest variables and their associated latent variables (there is no regression...