Renyi’s Entropy, Divergence and Their Nonparametric Estimators

2010 ◽  
pp. 47-102 ◽  
Author(s):  
Dongxin Xu ◽  
Deniz Erdogmuns
Keyword(s):  
Nonparametric Estimators ◽  
Renyi’S Entropy

scholarly journals Impact of mid Eocene greenhouse warming on America’s southernmost floras

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Damián A. Fernández ◽  
Luis Palazzesi ◽  
M. Sol González Estebenet ◽  
M. Cristina Tellería ◽  
Viviana D. Barreda
Keyword(s):  
Middle Eocene ◽  
Pollen Grains ◽  
Late Eocene ◽  
Significant Rise ◽  
Spores And Pollen ◽  
Deep Time ◽  
Nonparametric Estimators ◽  
Southern Patagonia ◽  
Marine Realm ◽  
High Diversity

AbstractA major climate shift took place about 40 Myr ago—the Middle Eocene Climatic Optimum or MECO—triggered by a significant rise of atmospheric CO2 concentrations. The biotic response to this MECO is well documented in the marine realm, but poorly explored in adjacent landmasses. Here, we quantify the response of the floras from America’s southernmost latitudes based on the analysis of terrestrially derived spores and pollen grains from the mid-late Eocene (~46–34 Myr) of southern Patagonia. Robust nonparametric estimators indicate that floras in southern Patagonia were in average ~40% more diverse during the MECO than pre-MECO and post-MECO intervals. The high atmospheric CO2 and increasing temperatures may have favored the combination of neotropical migrants with Gondwanan species, explaining in part the high diversity that we observed during the MECO. Our reconstructed biota reflects a greenhouse world and offers a climatic and ecological deep time scenario of an ice-free sub-Antarctic realm.

On weighted Renyi’s entropy for double-truncated distribution

2018 ◽  
Vol 48 (10) ◽  
pp. 2562-2579
Author(s):  
Shivangi Singh ◽  
Chanchal Kundu
Keyword(s):  
Truncated Distribution ◽  
Renyi’S Entropy

scholarly journals Optimal designs for nonparametric estimation of zeros of regression functions

2012 ◽  
Vol 51 (1) ◽  
pp. 55-65
Author(s):  
Zdeněk Hlávka
Keyword(s):  
Confidence Interval ◽  
Nonparametric Estimation ◽  
Regression Function ◽  
Optimal Designs ◽  
Nonparametric Estimators ◽  
Regression Functions ◽  
Expected Width

ABSTRACT We investigate nonparametric estimators of zeros of a regression function and its derivatives and we derive the distribution of design points minimizing the expected width of a confidence interval and the expected variance of the proposed estimator.

scholarly journals Parameter estimation of ODE’s via nonparametric estimators

2008 ◽  
Vol 2 (0) ◽  
pp. 1242-1267 ◽  
Author(s):  
Nicolas J-B. Brunel
Keyword(s):  
Parameter Estimation ◽  
Nonparametric Estimators

scholarly journals Evaluating Stochastic Seeding Strategies in Networks

2021 ◽  
Author(s):  
Alex Chin ◽  
Dean Eckles ◽  
Johan Ugander
Keyword(s):  
Field Experiments ◽  
Random Network ◽  
Data Driven ◽  
Large Field ◽  
Special Issue ◽  
Valid Inference ◽  
Nonparametric Estimators ◽  
Prescriptive Analytics ◽  
Marketing Intervention ◽  
Random Individual

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.

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

2021 ◽  
Vol 2068 (1) ◽  
pp. 012003
Author(s):  
Ayari Samia ◽  
Mohamed Boutahar
Keyword(s):  
Distribution Function ◽  
Empirical Distribution Function ◽  
Extreme Values ◽  
Empirical Distribution ◽  
Extreme Value ◽  
Dependence Function ◽  
Marginal Distributions ◽  
Nonparametric Estimators ◽  
Monte Carlo Experiments ◽  
Multivariate Extreme Values

Abstract 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.

Sign in / Sign up

Export Citation Format

Share Document