scholarly journals Voxel-wise and spatial modelling of binary lesion masks: A review and comparison of methods

2021 ◽  
Author(s):  
Petya Kindalova ◽  
Ioannis Kosmidis ◽  
Thomas E. Nichols
Keyword(s):  
Maximum Likelihood ◽  
Spatial Dependence ◽  
Reference Data ◽  
Spatial Modelling ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Set ◽  
Lesion Mapping ◽  
Modelling Approach

AbstractObjectivesWhite matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.MethodsWe review three regression approaches for modelling binary lesion masks including massunivariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we suggest matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.ResultsOur findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.ConclusionBias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.

scholarly journals Generalized Weighted Rama Distribution: Properties and Application to Model Lifetime Data

2021 ◽  
pp. 9-37
Author(s):  
Samuel U. Enogwe ◽  
Chisimkwuo John ◽  
Happiness O. Obiora-Ilouno ◽  
Chrisogonus K. Onyekwere
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Estimation Of Parameters ◽  
Data Set ◽  
Bathtub Shape ◽  
Asymptotic Confidence Intervals

In this paper, we propose a new lifetime distribution called the generalized weighted Rama (GWR) distribution, which extends the two-parameter Rama distribution and has the Rama distribution as a special case. The GWR distribution has the ability to model data sets that have positive skewness and upside-down bathtub shape hazard rate. Expressions for mathematical and reliability properties of the GWR distribution have been derived. Estimation of parameters was achieved using the method of maximum likelihood estimation and a simulation was performed to verify the stability of the maximum likelihood estimates of the model parameters. The asymptotic confidence intervals of the parameters of the proposed distribution are obtained. The applicability of the GWR distribution was illustrated with a real data set and the results obtained show that the GWR distribution is a better candidate for the data than the other competing distributions being investigated.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals A Family of Loss Distributions with an Application to the Vehicle Insurance Loss Data

2019 ◽  
pp. 731-744 ◽  
Author(s):  
Zubair Ahmad Ahmad ◽  
Eisa Mahmoudi Mahmoudi ◽  
G. G. Hamedani
Keyword(s):  
Maximum Likelihood ◽  
Financial Risk ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Set ◽  
Loss Model ◽  
New Class ◽  
Proposed Model ◽  
Loss Distributions ◽  
Loss Data

Actuaries are often in search of nding an adequate loss model in the scenario of actuarial and financial risk management problems. In this work, we propose a new approach to obtain a new class of loss distributions. A special sub-model of the proposed family, called the Weibull-loss model isconsidered in detail. Some mathematical properties are derived and maximum likelihood estimates of the model parameters are obtained. Certain characterizations of the proposed family are also provided. A simulation study is done to evaluate the performance of the maximum likelihood estimators. Finally, an application of the proposed model to the vehicle insurance loss data set is presented.

scholarly journals The Weibull Birnbaum-Saunders Distribution And Its Applications

2020 ◽  
Vol 9 (1) ◽  
pp. 61-81
Author(s):  
Lazhar BENKHELIFA
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Reliability Estimation ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Proposed Model ◽  
Modeling Data

A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

scholarly journals On Modified Burr XII-Inverse Weibull Distribution: Development, Properties, Characterizations and Applications

2020 ◽  
pp. 721-735
Author(s):  
Fiaz Ahmad Bhatti ◽  
G. G. Hamedani ◽  
Haitham M. Yousof ◽  
Azeem Ali ◽  
Munir Ahmad
Keyword(s):  
Maximum Likelihood ◽  
Hazard Rate ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Lifetime Distribution ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Data Sets ◽  
Quarterly Earnings ◽  
Inverse Weibull Distribution

A flexible lifetime distribution with increasing, decreasing, inverted bathtub and modified bathtub hazard rate called Modified Burr XII-Inverse Weibull (MBXII-IW) is introduced and studied. The density function of MBXII-IW is exponential, left-skewed, right-skewed and symmetrical shaped.  Descriptive measures on the basis of quantiles, moments, order statistics and reliability measures are theoretically established. The MBXII-IW distribution is characterized via different techniques. Parameters of MBXII-IW distribution are estimated using maximum likelihood method. The simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). The potentiality of MBXII-IW distribution is demonstrated by its application to real data sets: serum-reversal times and quarterly earnings.

scholarly journals An updated global COI barcode reference data set for Fall Armyworm (Spodoptera frugiperda) and first record of this species in Bhutan

2020 ◽  
Author(s):  
Kiran Mahat ◽  
Andrew Mitchell ◽  
Tshelthrim Zangpo
Keyword(s):  
Spodoptera Frugiperda ◽  
Reference Data ◽  
Dna Barcode ◽  
Fall Armyworm ◽  
First Record ◽  
Molecular Techniques ◽  
Data Sets ◽  
Pest Species ◽  
Maize Crop ◽  
Data Set

AbstractWe report the first detection of Fall Armyworm (FAW), Spodoptera frugiperda (Smith, 1797), in Bhutan. FAW feeds on more than 300 plant species and is a serious pest of many. It has been spreading through Africa since 2016 and Asia since 2018. In Bhutan, this species was first detected in maize fields in the western part of the country in September 2019 and subsequently found infesting maize crop in southern parts of the country in December 2019 and April 2020. Using morphological and molecular techniques the presence of the first invading populations of S. frugiperda in Bhutan is confirmed through this study. We present an updated reference DNA barcode data set for FAW comprising 374 sequences, which can be used to reliably identify this serious pest species, and discuss some of the reasons why such compiled reference data sets are necessary, despite the publicly availability of the underlying data. We also report on a second armyworm species, the Northern Armyworm, Mythimna separata (Walker, 1865), in rice, maize and other crops in eighteen districts of Bhutan.

An application of Bayesian techniques to the genetic evaluation of growth traits in Eucalyptus globulus

1998 ◽  
Vol 28 (9) ◽  
pp. 1286-1294 ◽  
Author(s):  
F Soria ◽  
F Basurco ◽  
G Toval ◽  
L Silió ◽  
M C Rodriguez ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Eucalyptus Globulus ◽  
Maximum Likelihood Estimates ◽  
Breeding Value ◽  
Likelihood Method ◽  
Phenotypic Correlation ◽  
Data Set ◽  
Breeding Values ◽  
Bayesian Techniques ◽  
Self Pollination

A Bayesian procedure coupled with Gibbs sampling was implemented to obtain inferences about genetic parameters and breeding values for height and diameter of 7-year-old Eucalyptus globulus Labill. is described. The data set consisted of 21 708 trees from 260 open-pollinated families taken from 10 different Australian provenances, from one Spanish population, and from two clones. The trees are distributed over eight sites in the south of Spain, with 20 blocks per site. Data were corrected for heterogeneity of phenotypic variances between blocks. In the analysis, a self-pollination rate of 30% for the open-pollinated families is assumed in the relationship matrix. The posterior means (and standard deviations) of the heritabilities of height and diameter and the genetic and phenotypic correlation were 0.217 (0.014), 0.128 (0.084), 0.768 (0.028), and 0.799 (0.003). Results from the standard restricted maximum likelihood method were 0.173, 0.113, 0.759, and 0.798, respectively. Most of the discrepancy in heritability estimates from both methods can be attributed to the adjustement of residual maximum likelihood estimates to the assumed self-pollination rate, which ignores the presence of clones in the trial. The effect of the method of prediction of breeding values (best linear unbiased prediction or Bayesian techniques) on the genetic superiority of the selected trees was not important. Differences in breeding value among provenances and among families were evidenced for both traits.

The effects of halothane susceptibility on some economically important traits in pigs 1. Litter productivity

1985 ◽  
Vol 40 (2) ◽  
pp. 351-358 ◽  
Author(s):  
A. E. Carden ◽  
W. G. Hill ◽  
A. J. Webb
Keyword(s):  
Linkage Disequilibrium ◽  
Maximum Likelihood ◽  
Genetic Differentiation ◽  
Perinatal Mortality ◽  
Litter Size ◽  
Maximum Likelihood Estimates ◽  
Halothane Anaesthesia ◽  
Data Set ◽  
Apparent Influence

ABSTRACTThe effects of susceptibility to halothane anaesthesia on litter productivity were investigated by comparing susceptible and normal females in two sets of data. The first comprised 206 litters from the first five generations of Pietrain/Hampshire synthetic lines selected for and against halothane susceptibility. Susceptible and normal females were mated to boars of their own type. The second data set consisted of 93 litters from the same susceptible and normal females mated to normal boars. Compared with normal contemporaries, litter sizes of susceptible females were reduced by 1·16 (s.e. 0·40) piglets at birth, and 1-76 (s.e. 0·41) at weaning (ca. 1 weeks). Maximum likelihood estimates of the proportions of piglet deaths from birth to weaning as a trait of susceptible v. normal dams were 0·32 v. 014 (P < 0·001). There were no significant differences in piglet weights or perinatal mortality, and no apparent influence of piglet genotype on any trait. The lower litter size of susceptible females at weaning appeared to result from reductions in both numbers born and survival to weaning. The study bears out previous reports of a reduction in litter productivity due to the halothane gene. However, the present differences could have arisen largely from random genetic differentiation between lines, or linkage disequilibrium in the synthetic foundation population.

scholarly journals Simple penalties on maximum likelihood estimates of genetic parameters to reduce sampling variation

2015 ◽  
Author(s):  
karin meyer
Keyword(s):  
Maximum Likelihood ◽  
Genetic Parameters ◽  
Likelihood Function ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Simple Modification ◽  
Scale Free ◽  
Wide Range ◽  
Sampling Variation ◽  
The Difference

Multivariate estimates of genetic parameters are subject to substantial sampling variation, especially for smaller data sets and more than a few traits. A simple modification of standard, maximum likelihood procedures for multivariate analyses to estimate genetic covariances is described, which can improve estimates by substantially reducing their sampling variances. This is achieved maximizing the likelihood subject to a penalty. Borrowing from Bayesian principles, we propose a mild, default penalty -- derived assuming a Beta distribution of scale-free functions of the covariance components to be estimated -- rather than laboriously attempting to determine the stringency of penalization from the data. An extensive simulation study is presented demonstrating that such penalties can yield very worthwhile reductions in loss, i.e. the difference from population values, for a wide range of scenarios and without distorting estimates of phenotypic covariances. Moreover, mild default penalties tend not to increase loss in difficult cases and, on average, achieve reductions in loss of similar magnitude than computationally demanding schemes to optimize the degree of penalization. Pertinent details required for the adaptation of standard algorithms to locate the maximum of the likelihood function are outlined.

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