Model-based approach for household clustering with mixed scale variables

The Ministry of Social Development in Mexico is in charge of creating and assigning social programmes targeting specific needs in the population for the improvement of the quality of life. To better target the social programmes, the Ministry is aimed to find clusters of households with the same needs based on demographic characteristics as well as poverty conditions of the household. Available data consists of continuous, ordinal, and nominal variables, all of which come from a non-i.i.d complex design survey sample. We propose a Bayesian nonparametric mixture model that jointly models a set of latent variables, as in an underlying variable response approach, associated to the observed mixed scale data and accommodates for the different sampling probabilities. The performance of the model is assessed via simulated data. A full analysis of socio-economic conditions in households in the Mexican State of Mexico is presented

Bayesian multivariate mixed-scale density estimation

Although continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is limited theory on multivariate mixed scale density estimation. In this note, we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lebesgue measure. For the proposed class of priors, we provide sufficient conditions for large support, strong consistency and rates of posterior contraction. These conditions allow one to convert sufficient conditions obtained in the setting of multivariate continuous density estimation to the mixed scale case. To illustrate the procedure a rounded multivariate nonparametric mixture of Gaussians is introduced and applied to a crime and communities dataset

A critical cluster analysis of 44 indicators of author-level performance

This paper explores the relationship between author-level bibliometric indicators and the researchers the "measure", exemplified across five academic seniorities and four disciplines. Using cluster methodology, the disciplinary and seniority appropriateness of author-level indicators is examined. Publication and citation data for 741 researchers across Astronomy, Environmental Science, Philosophy and Public Health was collected in Web of Science (WoS). Forty-four indicators of individual performance were computed using the data. A two-step cluster analysis using IBM SPSS version 22 was performed, followed by a risk analysis and ordinal logistic regression to explore cluster membership. Indicator scores were contextualized using the individual researcher's curriculum vitae. Four different clusters based on indicator scores ranked researchers as low, middle, high and extremely high performers. The results show that different indicators were appropriate in demarcating ranked performance in different disciplines. In Astronomy the h2 indicator, sum pp top prop in Environmental Science, Q2 in Philosophy and e-index in Public Health. The regression and odds analysis showed individual level indicator scores were primarily dependent on the number of years since the researcher's first publication registered in WoS, number of publications and number of citations. Seniority classification was secondary therefore no seniority appropriate indicators were confidently identified. Cluster methodology proved useful in identifying disciplinary appropriate indicators providing the preliminary data preparation was thorough but needed to be supplemented by other analyses to validate the results. A general disconnection between the performance of the researcher on their curriculum vitae and the performance of the researcher based on bibliometric indicators was observed.Comment: 28 pages, 7 tables, 2 figures, 2 appendice

Geographic Patterns of Early Holocene New World Dental Morphological Variation

Dental anthropology played a seminal role in early studies of the peopling of the New World, and was a foundation of the early three wave model proposed by Greenberg, Turner and Zegura. In recent years, however, developments in anthropological genetics, craniometry, and archaeological discoveries have largely omit-ted dental anthropology from debates regarding Native American origins. Here we consider this situation and reassert dental anthropology\u27s relevance to the topic by presenting an inter-individual analysis of Paleoindian and Paleoamerican dentitions. A small set of dental morphological variables was used to estimate Gower similarity coefficients between individual specimens. The resulting similarity matrix was ordinated using multidimensional scaling; all analyses were per-formed in Clustan v. 7.05. While results should be considered preliminary, patterns of variation suggest morphological similarity along both coasts of North and South America with a somewhat distinct grouping of North American Paleoindians deriving from more inland portions of the continent. This pattern is consistent with recent genetic scenarios, notably the bicoastal model presented by O\u27Rourke and Raff (2010), which indicates that Paleoindians may have taken multiple migration routes from Beringia, moving along both coasts as well as through the ice free corridor. Future studies may build on this work to reintegrate dental data and analysis into research concerning the peopling of the New World

The Clustering of Households in Madura Based on Factors Affecting Their Ingestion of Clean Water Using Similarity Weight and Filter Method

Clean Water and Sanitation is one of SDGs’ indicators that relates to human’ demand for clean water. Three of four regencies in Madura Island reportedly have suffered in drought, thus it leads this research to fulfill Madura people need of water. Madura Island has 3097 households in need of water. However, not all households could fetch their need. This research aims to classify the households of Madura Island regarding factors which affect their ingestion of clean water using cluster analysis. There are clustering numerical data and categorical data. Therefore, this research uses Similarity Weight and Filter Method. SWFM is one of clustering mix methods in which there are clustering numerical, using hierarchical ward, and clustering categorical, using k-modes. To analyze the clustering numerical data, there are 3 variables and it gains two optimum groups by using ward method with pseudo-F 1001,172. Clustering categorical analysis uses 6 variables with k-modes and gains three groups and SWFM gains five groups. Five groups are selected because they produced the smallest ratio 0,006627 in the group

Mutual interference is common and mostly intermediate in magnitude

<p>Abstract</p> <p>Background</p> <p>Interference competition occurs when access to resources is negatively affected by the presence of other individuals. Within a species or population, this is known as mutual interference, and it is often modelled with a scaling exponent, <it>m</it>, on the number of predators. Originally, mutual interference was thought to vary along a continuum from prey dependence (no interference; <it>m </it>= 0) to ratio dependence (<it>m </it>= -1), but a debate in the 1990's and early 2000's focused on whether prey or ratio dependence was the better simplification. Some have argued more recently that mutual interference is likely to be mostly intermediate (that is, between prey and ratio dependence), but this possibility has not been evaluated empirically.</p> <p>Results</p> <p>We gathered estimates of mutual interference from the literature, analyzed additional data, and created the largest compilation of unbiased estimates of mutual interference yet produced. In this data set, both the alternatives of prey dependence and ratio dependence were observed, but only one data set was consistent with prey dependence. There was a tendency toward ratio dependence reflected by a median <it>m </it>of -0.7 and a mean <it>m </it>of -0.8.</p> <p>Conclusions</p> <p>Overall, the data support the hypothesis that interference is mostly intermediate in magnitude. The data also indicate that interference competition is common, at least in the systems studied to date. Significant questions remain regarding how different factors influence interference, and whether interference can be viewed as a characteristic of a particular population or whether it generally shifts from low to high levels as populations increase in density.</p

SELECTION OF THE BEST SEM MODEL TO IDENTIFY FACTORS AFFECTING MARKETING PERFORMANCE IN THE ICT INDUSTRY

The digital revolution in society and the advances in marketing practices create tremendous challenges for companies and even more so for Information and Communication Technology (ICT) service providers. They are faced with increasingly complex and rapidly changing market competition, knowing these problems can use SEM to form a research model and find out the relationship between latent variables and their indicators. The purpose of this study is to identify the best structural equation model that can describe Marketing Performance in the ICT Industry in Indonesia. The data used in this study is primary data obtained from the results of distributing offline and online questionnaires to 300 management levels working in the ICT Industry. The methods compared in this study are Covariance Based Structural Equation Modeling and Partial Least Square Structural Equation Modeling. The results showed that the best model to determine the factors that influence Marketing Performance in the ICT Industry in Indonesia is PLS-SEM with the goodness-of-fit model R2 for the latent variable Marketing Performance is 0.436. This shows that the accuracy of the variables CEM, DBI and DOE together in predicting MP variables is relatively weak. Based on the PLS-SEM model, it is found that Digital Operational Excellence is a mediator that can increase the influence of Customer Experience Management on Marketing Performance. Meanwhile, Digital Business Innovation has no significant effect in increasing the influence of Customer Experience Management on Marketing Performance. The novelty of this research is the development of the best SEM models (CB-SEM and PLS-SEM) in the field of Information and Communication Technology in Indonesia