USING INTERACTION IN TWO-WAY DATA TABLES

Agronomists and breeders frequently collect yield data for a number of genotypes in a number of environments (site-years), resulting in a two-way data table. The Additive Main effects and Multiplicative Interaction (AMMI) model combines regular analysis of variance (ANOVA) for additive main effects with principal components analysis (PCA) for multiplicative structure within the interaction (that is, within the residual from ANOVA). AMMI is effective for (1) understanding genotype-environment interaction, (2) improving the accuracy of yield estimates, (3) increasing the probability of successfully selecting genotypes with the highest yields, (4) imputing missing data, and (5) increasing the flexibility and efficiency of experimental designs. Ultimately these advantages imply larger selection gains in breeding research and more reliable recommendations in agronomy research. AMMI is ordinarily the statistical method of choice when main effects and interaction are both important

Imputation Framework for Missing Values

Abstract-Missing values may occur for several reasons and affects the quality of data, such as malfunctioning of measurement equipment, changes in experimental design during data collection, collation of several similar but not identical datasets and also when respondents in a survey may refuse to answer certain questions such as age or income. Missing values in datasets can be taken as a common problem in statistical analysis. This paper first proposes the analysis of broadly used methods to treat missing values which are either continuous or discrete. And then, an estimator is advocated to impute both continuous and discrete missing target values. The proposed method is evaluated to demonstrate that the approach is better than existing methods in terms of classification accuracy

A comparative study of tree-based models for churn prediction : a case study in the telecommunication sector

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companies’ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models

A Human Development Index by Income Groups

human development, water, sanitation

A Human Development Index by Income Groups

One of the most frequent critiques of the HDI is that is does not take into account inequality within countries in its three dimensions. We suggest a relatively easy and intuitive approach which allows to compute the three components and the overall HDI for quintiles of the income distribution. This allows to compare the level in human development of the poor with the level of the non-poor within countries, but also across countries. An empirical illustration for a sample of 13 low and middle income countries and 2 industrialized countries shows that inequality in human development within countries is indeed high. The results also show that the level of inequality is only weakly correlated with the level of human development itself.Human Development, Income Inequality, Differential Mortality, Inequality in Education