Model-Based Cluster Analysis of Democracies

Model-Based Cluster Analysis of Democracies

We apply model-based cluster analysis to data concerning types of democracies, creating an instrument for typologies. Noting several advantages of modelbased clustering over traditional clustering methods, we fit a normal mixture model for types of democracy in the context of the majoritarian-consensus contrast using Lijphart’s (1999) data on ten variables for 36 democracies. The model for the ...

Interpolating time series based on fuzzy cluster analysis problem

This study proposes the model for interpolating time series to use them  to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in  M3-Competition data. Comparing  to the exist...

Inference in model-based cluster analysis

Ban eld and Raftery introduced a new approach to cluster analysis based on parsimo nious geometricmodeling of the within group covariance matrices in a mixture of multivariate normal distributions using hierarchical agglomeration and iterative relocation It works well and is widely used via the MCLUST software available in S PLUS and StatLib However it has several limitations there is no assess...

Model Based Cluster and Outlier Analysis

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...