Auto Insurance Tenure Prediction and Analysis

The purpose of this project is to understand the main factors that drive customer tenure within auto insurance industry for six or more years. The analysis is based on three years of the J.D. Power Auto Insurance survey data. For the analysis, multiple binary machine learning algorithms were implemented and measured to classify whether customers would stay with the same insurer for more than six years. Random forest was found to be the most robust model as compared to logistic regression, decision trees, and xgboost

Equations defining probability tree models

Coloured probability tree models are statistical models coding conditional independence between events depicted in a tree graph. They are more general than the very important class of context-specific Bayesian networks. In this paper, we study the algebraic properties of their ideal of model invariants. The generators of this ideal can be easily read from the tree graph and have a straightforward interpretation in terms of the underlying model: they are differences of odds ratios coming from conditional probabilities. One of the key findings in this analysis is that the tree is a convenient tool for understanding the exact algebraic way in which the sum-to-1 conditions on the parameter space translate into the sum-to-one conditions on the joint probabilities of the statistical model. This enables us to identify necessary and sufficient graphical conditions for a staged tree model to be a toric variety intersected with a probability simplex.Comment: 22 pages, 4 figure

Neighborhood and community interactions determine the spatial pattern of tropical tree seedling survival

Factors affecting survival and recruitment of 3531 individually mapped seedlings of Myristicaceae were examined over three years in a highly diverse neotropical rain forest, at spatial scales of 1–9 m and 25 ha. We found convincing evidence of a community compensatory trend (CCT) in seedling survival (i.e., more abundant species had higher seedling mortality at the 25-ha scale), which suggests that density-dependent mortality may contribute to the spatial dynamics of seedling recruitment. Unlike previous studies, we demonstrate that the CCT was not caused by differences in microhabitat preferences or life history strategy among the study species. In local neighborhood analyses, the spatial autocorrelation of seedling survival was important at small spatial scales (1–5 m) but decayed rapidly with increasing distance. Relative seedling height had the greatest effect on seedling survival. Conspecific seedling density had a more negative effect on survival than heterospecific seedling density and was stronger and extended farther in rare species than in common species. Taken together, the CCT and neighborhood analyses suggest that seedling mortality is coupled more strongly to the landscape-scale abundance of conspecific large trees in common species and the local density of conspecific seedlings in rare species. We conclude that negative density dependence could promote species coexistence in this rain forest community but that the scale dependence of interactions differs between rare and common species

Statistical Classification Techniques for Photometric Supernova Typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.Comment: 19 pages, 41 figures. No changes. Additional material and summary video available at http://cosmoaims.wordpress.com/2010/09/30/boosting-for-supernova-classification

Nest niche overlap among the endangered Vinaceous-breasted Parrot (Amazona vinacea) and sympatric cavity-using birds, mammals, and social insects in the subtropical Atlantic Forest, Argentina

Many forest bird species require tree cavities for nesting, and share this resource with a diverse community of animals. When cavities are limited, niche overlap can result in interspecific competition, with negative consequences for threatened populations. Vinaceous-breasted Parrots (Amazona vinacea) are endangered cavity nesters endemic to the subtropical Atlantic Forest, where cavities are scarce. We examined nest niche overlap among Vinaceous-breasted Parrots and 9 potential competitors (birds and mammals >140 g, and social insects) in Argentina, considering (1) timing of breeding, (2) characteristics of cavities (depth, entrance diameter, height), trees (diameter at breast height DBH, species, condition), and habitat (surrounding land use, distance to edge), and (3) interspecific cavity reuse. During 10 breeding seasons we studied nests and roosts, measured their characteristics, and monitored cavities to detect reuse. We used multinomial logistic regression to determine whether the 6 most abundant taxa differed in nest and roost site characteristics. Timing of breeding overlapped for all bird species except the White-eyed Parakeet (Psittacara leucophthalmus). No combination of cavity, tree, and habitat characteristics predicted the taxa that utilized cavities. Moreover, 8 of the 10 taxa reused cavities interspecifically. The high level of overlap in realized nest niche, combined with previous evidence that cavities could limit bird density in our study area, suggest the possibility of interspecific competition for cavities among multiple taxa. Although models did not perform well at classifying cavities by taxon, some characteristics of cavities, trees, and habitat were selected more by Vinaceous-breasted Parrots than by other taxa, and we recommend targeting conservation efforts toward cavities and trees with these characteristics (7-40 cm entrance diameter, >10 m high, DBH >55 cm). We found 62% of Vinaceous-breasted Parrot nests on farms (vs. ≤50% for other taxa), highlighting the importance of working with local farmers to conserve cavities in anthropogenic habitats as well as in protected areas.Fil: Bonaparte, Eugenia Bianca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del NOA. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Museo de Ciencias Naturales. Instituto de Bio y Geociencias del NOA; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Cockle, Kristina Louise. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del NOA. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Museo de Ciencias Naturales. Instituto de Bio y Geociencias del NOA; Argentina. University of British Columbia; Canad

Calculating and understanding the value of any type of match evidence when there are potential testing errors

It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases of such features, a serious challenge to the use of Bayes in such legal contexts is that its standard formulaic representations are not readily understandable to non-statisticians. Attempts to get round this problem usually involve representations based around some variation of an event tree. While this approach works well in explaining the most trivial instance of Bayes’ theorem (involving a single hypothesis and a single piece of evidence) it does not scale up to realistic situations. In particular, even with a single piece of match evidence, if we wish to incorporate the possibility that there are potential errors (both false positives and false negatives) introduced at any stage in the investigative process, matters become very complex. As a result we have observed expert witnesses (in different areas of speciality) routinely ignore the possibility of errors when presenting their evidence. To counter this, we produce what we believe is the first full probabilistic solution of the simple case of generic match evidence incorporating both classes of testing errors. Unfortunately, the resultant event tree solution is too complex for intuitive comprehension. And, crucially, the event tree also fails to represent the causal information that underpins the argument. In contrast, we also present a simple-to-construct graphical Bayesian Network (BN) solution that automatically performs the calculations and may also be intuitively simpler to understand. Although there have been multiple previous applications of BNs for analysing forensic evidence—including very detailed models for the DNA matching problem, these models have not widely penetrated the expert witness community. Nor have they addressed the basic generic match problem incorporating the two types of testing error. Hence we believe our basic BN solution provides an important mechanism for convincing experts—and eventually the legal community—that it is possible to rigorously analyse and communicate the full impact of match evidence on a case, in the presence of possible error