Exploiting Vulnerability Disclosures: Statistical Framework and Case Study

2016 ◽  
Author(s):  
MingJian Tang ◽  
Mamoun Alazab ◽  
Yuxiu Luo
Keyword(s):  
Statistical Framework

scholarly journals An Extended Maximum Likelihood Inference of Geographic Range Evolution by Dispersal, Local Extinction and Cladogenesis

2016 ◽  
Author(s):  
Champak Beeravolu Reddy ◽  
Fabien Condamine
Keyword(s):  
Local Extinction ◽  
Rapid Expansion ◽  
Model Complexity ◽  
Published Data ◽  
Computationally Efficient ◽  
Likelihood Methods ◽  
Origin And Evolution ◽  
Statistical Framework ◽  
Biological Realism

The origin and evolution of species ranges remains a central focus of historical biogeography and the advent of likelihood methods based on phylogenies has revolutionized the way in which range evolution has been studied. A decade ago, the first elements of what turned out to be a popular inference approach of ancestral ranges based on the processes of Dispersal, local Extinction and Cladogenesis (DEC) was proposed. The success of the DEC model lies in its use of a flexible statistical framework known as a Continuous Time Markov Chain and since, several conceptual and computational improvements have been proposed using this as a baseline approach. In the spirit of the original version of DEC, we introduce DEC eXtended (DECX) by accounting for rapid expansion and local extinction as possible anagenetic events on the phylogeny but without increasing model complexity (i.e. in the number of free parameters). Classical vicariance as a cladogenetic event is also incorporated by making use of temporally flexible constraints on the connectivity between any two given areas in accordance with the movement of landmasses and dispersal opportunity over time. DECX is built upon a previous implementation in C/C++ and can analyze phylogenies on the order of several thousand tips in a few minutes. We test our model extensively on Pseudo Observed Datasets and on well-curated and recently published data from various island clades and a worldwide phylogeny of Amphibians (3309 species). We also propose the very first implementation of the DEC model that can specifically account for trees with fossil tips (i.e. non-ultrametric) using the phylogeny of palpimanoid spiders as a case study. In this paper, we argue in favour of the proposed improvements, which have the advantage of being computationally efficient while toeing the line of increased biological realism.

scholarly journals MODELING BEACH PROFILE EVOLUTION – A STATISTICAL–PROCESS BASED APPROACH

2012 ◽  
Vol 1 (33) ◽  
pp. 13
Author(s):  
Douglas Pender ◽  
Harshinie Karunarathna
Keyword(s):  
Statistical Modeling ◽  
Storm Event ◽  
Beach Profile ◽  
Individual Event ◽  
Statistical Process ◽  
Statistical Framework ◽  
Narrabeen Beach ◽  
Process Based Models

This paper presents a new combined statistical-process based approach for modeling storm driven, cross-shore beach profile response. The approach discussed here involves combining detailed statistical modeling of offshore storm data and a process based morphodynamic model (XBeach), to assess the medium to long-term morphodynamic response of cross-shore beach profiles. Up until now the use of process-based models has been curtailed at the storm event timescale. This approach allows inclusion of the post-storm recovery, in addition to individual event impacts, thus allowing longer-term predictions. The calibration of XBeach for modeling, both, storm induced erosion and post- storm recovery, taking Narrabeen Beach, NSW, Australia as a case study; and the approach used to combine XBeach with the statistical framework to develop the approach are discussed.

scholarly journals Homoploid hybridization signals due to ancestral subdivision: a case study on the D lineage in wheat

2019 ◽  
Author(s):  
Yunfeng Jiang ◽  
Zhongwei Yuan ◽  
Haiyan Hu ◽  
Xueling Ye ◽  
Zhi Zheng ◽  
...  
Keyword(s):  
Hybrid Speciation ◽  
Genome Sequences ◽  
False Detection ◽  
Statistical Framework ◽  
Homoploid Hybridization ◽  
Wide Range ◽  
Evolutionary Studies ◽  
Homoploid Hybrid

AbstractHomoploid hybrid speciation has been reported in a wide range of species since the exploitation of genome sequences in evolutionary studies. However, the interference of ancestral subdivision has not been adequately considered in many such investigations. Using the D lineage in wheat as an example, we showed clearly that ancestral subdivision has led to false detection of homoploid hybridization signals. We develop a novel statistical framework by examining the changes in shared ancestral variations and infer on the likelihood of speciation due to genuine homoploid hybridization or ancestral subdivisions. Applying this to wheat data, we found that homoploid hybridization was not involved in the origin of the D lineage contrary to the now widely held belief. This example indicates that the significance of homoploid hybrid speciation is likely exaggerated. The underlying methodology developed in this study should be valuable for clarifying whether homoploid hybridization has contributed to the speciation of many other species.

A framework for assessing the ability to detect macroscale effects on fish growth

2020 ◽  
Author(s):  
Danielle L. Massie ◽  
Yan Li ◽  
Tyler Wagner
Keyword(s):  
Life Histories ◽  
Fish Growth ◽  
Environmental Drivers ◽  
Biotic Factors ◽  
Demographic Rates ◽  
Statistical Framework ◽  
Generalized Framework ◽  
Abiotic And Biotic Factors ◽  
Study Species

Various abiotic and biotic factors affect fish and their habitats at macroscales. For example, changes in global temperatures will likely alter demographic rates, including growth. However, to date, there is no statistical framework for assessing the ability to detect macroscale effects on fish growth under different sampling scenarios. We provide a generalized framework for calculating the frequentist and Bayesian power of detecting macroscale effects on fish growth. We illustrate this framework for a range of sampling scenarios which varied in the number of fish sampled per lake, the number of lakes sampled, and the magnitude of the temperature effect on growth for two case study species. However, the framework can be adapted to investigate other species, sampling scenarios, and environmental drivers. The ability to detect macroscale effects was more affected by the number of lakes sampled, rather than the number of fish sampled from each lake. Confidently detecting macroscale effects likely requires sampling hundreds of lakes. This was true for both case study species, despite different life histories and extents of spatial variability in growth.

scholarly journals A note on analysis of extreme minimum temperatures with the GAMLSS framework

2019 ◽  
Vol 67 (6) ◽  
pp. 1599-1604 ◽  
Author(s):  
Jesper Rydén
Keyword(s):  
Information Criteria ◽  
Additive Models ◽  
Large Set ◽  
Model Choice ◽  
Extreme Value Distributions ◽  
Statistical Framework ◽  
Return Levels ◽  
The Earth ◽  
Wide Range

Abstract Estimation of return levels, based on extreme value distributions, is of importance in the earth and environmental sciences. To incorporate non-stationarity in the modelling, the statistical framework of generalised additive models for location, scale and shape is an option, providing flexibility and with a wide range of distributions implemented. With a large set of selections possible, model choice is an issue. As a case study, we investigate annual minimum temperatures from measurements at a location in northern Sweden. For practical work, it turns out that care must be taken in examining the obtained distributions, not solely relying on information criteria. A simulation study illustrates the findings.

scholarly journals A roadmap for a quantitative ecosystem-based environmental impact assessment

2017 ◽  
Vol 74 (7) ◽  
pp. 2012-2023 ◽  
Author(s):  
J. Coston-Guarini ◽  
J.-M. Guarini ◽  
Shawn Hinz ◽  
Jeff Wilson ◽  
L. Chauvaud
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Environmental Changes ◽  
Ecosystem Model ◽  
Ecological Processes ◽  
Process Dynamics ◽  
Statistical Framework ◽  
Qualitative Approaches

Abstract A new roadmap for quantitative methodologies of Environmental Impact Assessment (EIA) is proposed, using an ecosystem-based approach. EIA recommendations are currently based on case-by-case rankings, distant from statistical methodologies, and ecological ideas that lack proof of generality or predictive capacities. These qualitative approaches ignore process dynamics, scales of variations and interdependencies and are unable to address societal demands to link socio-economic and ecological processes (e.g. population dynamics). We propose to re-focus EIA around the systemic formulation of interactions between organisms (organized in populations and communities) and their environments but inserted within a strict statistical framework. A systemic formulation allows scenarios to be built that simulate impacts on chosen receptors. To illustrate the approach, we design a minimum ecosystem model that demonstrates nontrivial effects and complex responses to environmental changes and validated with case study. We suggest that an Ecosystem-Based EIA—in which the socio-economic system is an evolving driver of the ecological one—is more promising than a socio-economic-ecological system where all variables are treated as equal. This refocuses the debate on cause-and-effect, processes, identification of essential portable variables, and allows for quantitative comparisons between projects, which is critical in cumulative effects determinations.

scholarly journals A General Statistical Framework for Mapping Quantitative Trait Loci in Nonmodel Systems: Issue for Characterizing Linkage Phases

Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 901-913 ◽  
Author(s):  
Min Lin ◽  
Xiang-Yang Lou ◽  
Myron Chang ◽  
Rongling Wu
Keyword(s):  
Genetic Research ◽  
Complex Trait ◽  
Forest Tree ◽  
Simultaneous Estimation ◽  
Linkage Phase ◽  
Statistical Framework ◽  
The Em Algorithm ◽  
Maximum Likelihood Methods ◽  
General Statistical

Abstract Because of uncertainty about linkage phases of founders, linkage mapping in nonmodel, outcrossing systems using molecular markers presents one of the major statistical challenges in genetic research. In this article, we devise a statistical method for mapping QTL affecting a complex trait by incorporating all possible QTL-marker linkage phases within a mapping framework. The advantage of this model is the simultaneous estimation of linkage phases and QTL location and effect parameters. These estimates are obtained through maximum-likelihood methods implemented with the EM algorithm. Extensive simulation studies are performed to investigate the statistical properties of our model. In a case study from a forest tree, this model has successfully identified a significant QTL affecting wood density. Also, the probability of the linkage phase between this QTL and its flanking markers is estimated. The implications of our model and its extension to more general circumstances are discussed.

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