scholarly journals Estimating shifts in diversification rates based on higher-level phylogenies

2016 ◽  
Vol 12 (10) ◽  
pp. 20160273 ◽  
Author(s):  
Tanja Stadler ◽  
Jana Smrckova
Keyword(s):  
Maximum Likelihood ◽  
Simulation Study ◽  
Time Dependent ◽  
Diversification Rates ◽  
The Past ◽  
Species Phylogeny ◽  
Family Level ◽  
Past State

Macroevolutionary studies recently shifted from only reconstructing the past state, i.e. the species phylogeny, to also infer the past speciation and extinction dynamics that gave rise to the phylogeny. Methods for estimating diversification dynamics are sensitive towards incomplete species sampling. We introduce a method to estimate time-dependent diversification rates from phylogenies where clades of a particular age are represented by only one sampled species. A popular example of this type of data is phylogenies on the genus- or family-level, i.e. phylogenies where one species per genus or family is included. We conduct a simulation study to validate our method in a maximum-likelihood framework. Further, this method has already been introduced into the Bayesian package M r B ayes , which led to new insights into the evolution of Hymenoptera.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

The insects of plant-held waters: a review and bibliography

2001 ◽  
Vol 17 (2) ◽  
pp. 241-260 ◽  
Author(s):  
HAROLD F. GREENEY
Keyword(s):  
Review Of The Literature ◽  
Classification Schemes ◽  
The Past ◽  
The Family ◽  
Family Level ◽  
Geographic Regions ◽  
Extensive Bibliography

Phytotelmata habitats have been the focus of much research and are utilized by a wide variety of taxa. In the past 15 years numerous studies in many geographic regions and covering various types of phytotelmata have greatly increased our understanding of these unique habitats. The most recent summary of phytotelmata inhabitants included over 20 families of insects. A review of the literature and extensive work in lowland Ecuador shows the family level diversity is in fact at least twice that reported earlier. A reassessment of previous phytotelmata classification schemes, as well as an extensive bibliography, is provided.

scholarly journals Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

2021 ◽  
Author(s):  
Julius S Ngwa ◽  
Howard J Cabral ◽  
Debbie M Cheng ◽  
David R Gagnon ◽  
Michael P LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

scholarly journals Estimating the Gumbel-Barnett copula parameter of dependence

2018 ◽  
Vol 41 (1) ◽  
pp. 53-73 ◽  
Author(s):  
Jennyfer Portilla Yela ◽  
José Rafael Tovar Cuevas
Keyword(s):  
Maximum Likelihood ◽  
Simulation Study ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Empirical Evaluation ◽  
Bayesian Estimator ◽  
Dependence Structure ◽  
Type I ◽  
Copula Parameter

In this paper, we developed an empirical evaluation of four estimation procedures for the dependence parameter of the Gumbel-Barnett copula obtained from a Gumbel type I distribution. We used the maximum likelihood, moments and Bayesian methods and studied the performance of the estimates, assuming three dependence levels and 20 different sample sizes. For each method and scenario, a simulation study was conducted with 1000 runs and the quality of the estimator was evaluated using four different criteria. A Bayesian estimator assuming a Beta(a,b) as prior distribution, showed the best performance regardless the sample size and the dependence structure.

scholarly journals Chiral Separation of rac-Propylene Oxide on Penicillamine Coated Gold NPs

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1716
Author(s):  
Nisha Shukla ◽  
Zachary Blonder ◽  
Andrew J. Gellman
Keyword(s):  
Propylene Oxide ◽  
Room Temperature ◽  
Adsorption Properties ◽  
Time Dependent ◽  
Prior Work ◽  
Au Nps ◽  
The Past ◽  
Racemic Mixtures ◽  
Enantioselective Adsorption ◽  
Gold Nps

The surfaces of chemically synthesized spherical gold NPs (Au-NPs) have been modified using chiral L- or D-penicillamine (Pen) in order to impart enantioselective adsorption properties. These chiral Au-NPs have been used to demonstrate enantioselective adsorption of racemic propylene oxide (PO) from aqueous solution. In the past we have studied enantioselective adsorption of racemic PO on L- or D-cysteine (Cys)-coated Au-NPs. This prior work suggested that adsorption of PO on Cys-coated Au-NPs equilibrates within an hour. In this work, we have studied the effect of time on the enantioselective adsorption of racemic PO from solution onto chiral Pen/Au-NPs. Enantioselective adsorption of PO on chiral Pen/Au-NPs is time-dependent but reaches a steady state after ~18 h at room temperature. More importantly, L- or D-Pen/Au-NPs are shown to adsorb R- or S-PO enantiospecifically and to separate the two PO enantiomers from racemic mixtures of RS-PO.

Illuminating the past: state of the art

2010 ◽  
Vol 14 (3) ◽  
pp. 155-182 ◽  
Author(s):  
Jassim Happa ◽  
Mark Mudge ◽  
Kurt Debattista ◽  
Alessandro Artusi ◽  
Alexandrino Gonçalves ◽  
...  
Keyword(s):  
State Of The Art ◽  
The Past ◽  
Past State
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