Feature combinations and the divergence criterion

Classifying large quantities of multidimensional remotely sensed agricultural data requires efficient and effective classification techniques and the construction of certain transformations of a dimension reducing, information preserving nature. The construction of transformations that minimally degrade information (i.e., class separability) is described. Linear dimension reducing transformations for multivariate normal populations are presented. Information content is measured by divergence

Child Survival, Poverty and Policy Options from DHS Surveys in Kenya: 1993-2003

This paper analyses multidimensional aspects of child poverty in Kenya. We carry out poverty and inequality comparisons for child survival and also use the parametric survival model to explain childhood mortality using DHS data. The results of poverty comparisons show that: children with the lowest probability of survival are from households with the lowest level of assets; and poverty orderings for child survival by assets are robust to the choice of the poverty line and to the measure of wellbeing. Inequality analysis suggests that there is less mortality inequality among children facing mortality than children who are better off. The survival model results show that child and maternal characteristics, and household assets are important correlates of childhood mortality. The results further show that health care services are crucial for child survival. Policy simulations suggest that there is potential for making some progress in reducing mortality, but the ERS and MDG targets cannot be achieved.Child survival, multidimensional poverty, inequality, stochastic dominance, childhood mortality, asset index, Kenya

Analysis of the Extent of Households’ Multidimensional Poverty: The Case of Nekemte City, Oromia, Ethiopia

The objective of the study was to examine the extent of households’ multidimensional poverty in Nekemte City. To achieve this objective, the study used both primary and secondary data. The primary data was collected from 379 sample household heads of Nekemte City through interview and questionnaire. It was based on cross sectional data collected during 2020/21. Alkire-Foster multidimensional poverty measurement: counting approach was employed to analyze the extent of multidimensional poverty in the study area. The result revealed that 20.6% of the population is under multidimensional poverty with average intensity and MPI of 41.5% and 8.55% respectively. Living standard dimension was the highest contributor to MPI followed by education dimension. Based on the findings, the study suggests improving economic activities, promoting access to education and improving saving habit. Moreover, improved targeting devices can be useful instruments in reducing poverty, in particular to reach those in severe poverty. Keywords: Multidimensional, poverty, Nekemte, city, Ethiopia, indicators, household DOI: 10.7176/EJBM/14-21-02 Publication date: November 30th 202

Determinants of Households’ Multidimensional Poverty: The Case of Nekemte City, Oromia, Ethiopia

The study aimed at analyzing determinants of households’ multidimensional poverty in Nekemte City. To achieve this objective, the study used both primary and secondary data. The primary data was collected using semi-structured questionnaire. Simple random sampling technique was followed to draw 379 sample household heads. For data analysis, both econometric and descriptive method was applied. From econometric models, binary logit regression model was employed. The logit model result indicated that household heads’ educational level, family size, dependency ratio, income, house ownership, saving habit and social capital are the major factors significantly influencing households’ multidimensional poverty in the city. Based on the findings, the study suggests improving economic activities, promoting access to education and improving saving habits. Moreover, improved targeting devices can be useful instruments in reducing multidimensional poverty, in particular to reach those in severe poverty. Keywords: Multidimensional poverty, logit, Ethiopia DOI: 10.7176/JESD/13-23-01 Publication date: December 31st 202

Determinants of Households’ Multidimensional Poverty: The Case of Nekemte City, Oromia, Ethiopia

The study aimed at analyzing determinants of households’ multidimensional poverty in Nekemte City. To achieve this objective, the study used both primary and secondary data. The primary data was collected using semi-structured questionnaire. Simple random sampling technique was followed to draw 379 sample household heads. For data analysis, both econometric and descriptive method was applied. From econometric models, binary logit regression model was employed. The logit model result indicated that household heads’ educational level, family size, dependency ratio, income, house ownership, saving habit and social capital are the major factors significantly influencing households’ multidimensional poverty in the city. Based on the findings, the study suggests improving economic activities, promoting access to education and improving saving habits. Moreover, improved targeting devices can be useful instruments in reducing multidimensional poverty, in particular to reach those in severe poverty. Keywords: Multidimensional poverty, logit, Ethiopia DOI: 10.7176/EJBM/14-21-01 Publication date: November 30th 202

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency, and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey furthermore a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled---provided the decay of temporal correlations is sufficiently well-resolved---within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise, are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.Comment: 26 pages, double columns; 15 figure

Bayesian Hyperbolic Multidimensional Scaling

Multidimensional scaling (MDS) is a widely used approach to representing high-dimensional, dependent data. MDS works by assigning each observation a location on a low-dimensional geometric manifold, with distance on the manifold representing similarity. We propose a Bayesian approach to multidimensional scaling when the low-dimensional manifold is hyperbolic. Using hyperbolic space facilitates representing tree-like structures common in many settings (e.g. text or genetic data with hierarchical structure). A Bayesian approach provides regularization that minimizes the impact of measurement error in the observed data and assesses uncertainty. We also propose a case-control likelihood approximation that allows for efficient sampling from the posterior distribution in larger data settings, reducing computational complexity from approximately $O(n^2)$ to $O(n)$. We evaluate the proposed method against state-of-the-art alternatives using simulations, canonical reference datasets, Indian village network data, and human gene expression data