Diversity of anura amphibians in an anthropic lake in the state of Paraíba, Brazil: distribution and temporal variation

The Caatinga is highly heterogeneous, many species being found in their regions. Much of anurofauna this area is commonly found in many open environments. In the present study both the diversity and the temporal occurrence of frogs were determined to Bela Vista Lagoon, located in the municipality of Cuité, Paraíba. 4 areas for sampling were marked, these being covered slowly by hiking. The naturalistic observations were conducted from May 2012 until April 2013 Methods of visual and auditory search were used to simultaneously capture and frequency of species. 6 frog species belonging to 4 genera were found distributed in three families: Bufonidae (2 species), Hylidae (2 species) and Leptodactylidae (2 species). Site 1 showed a wealth of three species, the other areas had a wealth equivalent of 5 species each. The anurofauna recorded high occupancy presented to water bodies and low associations zones altered by man. Nonparametric estimators, calculated for the 36 surveys for the pond Bela Vista, not reached its asymptote, but the Bootstrap model showed a tendency toward stabilization. Among the four sampled areas, Area 2 was the one with the highest diversity, areas 3 and 4 presented the lowest diversity, this fact being related to high dominance of species Rinnella jimi. Regarding the temporal distribution, amphibians showed up influenced by temperature and rainfall record for the region.

Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available

We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the sake of illustration, we present an application concerning patients hospitalized with COVID-19 in Galicia (Spain) during the first outbreak of the epidemic.

Nonparametric Estimation of Multivariate Extreme Value Copulas with Known and Unknown Marginal Distributions

The purpose of this paper is estimating the dependence function of multivariate extreme values copulas. Different nonparametric estimators are developed in the literature assuming that marginal distributions are known. However, this assumption is unrealistic in practice. To overcome the drawbacks of these estimators, we substituted the extreme value marginal distribution by the empirical distribution function. Monte Carlo experiments are carried out to compare the performance of the Pickands, Deheuvels, Hall-Tajvidi, Zhang and Gudendorf-Segers estimators. Empirical results showed that the empirical distribution function improved the estimators’ performance for different sample sizes.

Going beyond parametric regression in public management research

PurposePublic management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process. Without testing for those assumptions and consideration of semiparametric alternatives, such as maximum score, estimates might be biased, or predictions might not be as accurate as possible.Design/methodology/approachTo guide researchers, this paper provides an evaluative framework for comparing parametric estimators with semiparametric and nonparametric estimators for dichotomous dependent variables. To illustrate the framework, the article estimates the factors associated with the passage of school district bond referenda in all Texas school districts from 1998 to 2015.FindingsEstimates show that the correct prediction of a bond passing increases from 77.2 to 78%, with maximum score estimation relative to a commonly used parametric alternative. While this is a small increase, it is meaningful in comparison to the random prediction base model.Originality/valueFuture research modeling any dichotomous dependent variable can use the framework to identify the most appropriate estimator and relevant statistical programs.

Evaluating Stochastic Seeding Strategies in Networks

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated “off-policy” using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can substantially increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an antibullying intervention. This paper was accepted by Gui Liberali, Special Issue on Data-Driven Prescriptive Analytics.

Failure Extropy, Dynamic Failure Extropy and Their Weighted Versions

Extropy was introduced as a dual complement of the Shannon entropy. In this investigation, we consider failure extropy and its dynamic version. Various basic properties of these measures are presented. It is shown that the dynamic failure extropy characterizes the distribution function uniquely. We also consider weighted versions of these measures. Several virtues of the weighted measures are explored. Finally, nonparametric estimators are introduced based on the empirical distribution function.