scholarly journals Conditional distribution modeling as an alternative method for covariates simulation: comparison with joint multivariate normal and bootstrap techniques

2021 ◽  
Author(s):  
Giovanni Smania ◽  
E. Niclas Jonsson
Keyword(s):  
Conditional Distribution ◽  
Multivariate Normal ◽  
Distribution Modeling ◽  
Bootstrap Techniques ◽  
Alternative Method

scholarly journals Uncorrelated genetic drift of gene frequencies and linkage disequilibrium in some models of linked overdominant polymorphisms

1974 ◽  
Vol 24 (3) ◽  
pp. 281-294 ◽  
Author(s):  
Joseph Felsenstein
Keyword(s):  
Linkage Disequilibrium ◽  
Covariance Matrix ◽  
Genetic Drift ◽  
Conditional Distribution ◽  
Large Population ◽  
Multivariate Normal ◽  
Gene Frequencies ◽  
Constant Variance ◽  
Population Sizes ◽  
Multiplicative Family

SUMMARYFor large population sizes, gene frequencies p and q at two linked over-dominant loci and the linkage disequilibrium parameter D will remain close to their equilibrium values. We can treat selection and recombination as approximately linear forces on p, q and D, and we can treat genetic drift as a multivariate normal perturbation with constant variance-covariance matrix. For the additive-multiplicative family of two-locus models, p, q and D are shown to be (approximately) uncorrelated. Expressions for their variances are obtained. When selection coefficients are small the variances of p and q are those previously given by Robertson for a single locus. For small recombination fractions the variance of D is that obtained for neutral loci by Ohta & Kimura. For larger recombination fractions the result differs from theirs, so that for unlinked loci r2 ≃ 2/(3N) instead of 1/(2N). For the Lewontin-Kojima and Bodmer symmetric viability models, and for a model symmetric at only one of the loci, a more exact argument is possible. In the asymptotic conditional distribution in these cases, various of p, q and D are uncorrelated, depending on the type of symmetiy in the model.

scholarly journals Multiple imputation as an alternative method to simulate covariate distributions: comparison with joint multivariate normal and bootstrap techniques

2020 ◽  
Author(s):  
Giovanni Smania ◽  
E. Niclas Jonsson
Keyword(s):  
Drug Development ◽  
Multiple Imputation ◽  
Empirical Distribution ◽  
Development Program ◽  
Training Data ◽  
Clinical Trial Simulation ◽  
Multivariate Normal ◽  
Data Set ◽  
Internal Evaluation ◽  
Alternative Method

AbstractClinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVND). Here, we investigated the performance of an alternative method based on multiple imputation (MI). Age, weight, serum creatinine, creatinine clearance, sex and race data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation in terms of summary statistics. However, BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; MI was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. Improved extrapolation performance of the continuous covariates was observed for MI over MVND, yet after removing the healthy subject data from the training data set, there was no clear difference between the methods. Sex was better predicted by MVND vs. MI, while similar results were obtained for race. If CTS is used to simulate within the range of the observed distribution, the BS is the preferred method for covariates simulation. When extrapolating to new populations, a parametric method like MI/MVND is needed. As MVND rests on relatively strong assumptions, MI appears to be more robust when deviations from these assumptions occur.

On Characterizing the Multivariate Linear Exponential Distribution1

1969 ◽  
Vol 12 (5) ◽  
pp. 567-572 ◽  
Author(s):  
D. G. Kabe
Keyword(s):  
Exponential Distribution ◽  
Conditional Distribution ◽  
Column Vector ◽  
Multivariate Normal ◽  
Image Position ◽  
Linear Exponential Distribution

If x and y are independent p component column vectors, and the conditional distribution of x, given x+y = z, is known, what can be said about the distributions of x and y? This problem has been solved by Seshadri (1966) in the particular case when the conditional distribution of x, given x+y = z, is multivariate normal. In fact Seshadri′s paper implicitly contains a characterization of the multivariate linear exponential distribution(1)where A(x) is a function of x not involving the p component column vector w of constant terms.

scholarly journals Clarifying species dependence under joint species distribution modeling

2019 ◽  
Author(s):  
Alan E. Gelfand ◽  
Shinichiro Shirota
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modeling ◽  
Region Of Interest ◽  
Cape Floristic Region ◽  
Distribution Model ◽  
List Type ◽  
Multivariate Normal ◽  
Odds Ratios ◽  
Distribution Modeling

AbstractJoint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture species dependence through an associated correlation matrix arising from a set of latent multivariate normal variables. However, these associations offer little insight into dependence behavior between species at sites.We focus on presence/absence data using joint species modeling which incorporates spatial dependence between sites. For pairs of species, we emphasize the induced odds ratios (along with the joint probabilities of occurrence); they provide much clearer understanding of joint presence/absence behavior. In fact, we propose a spatial odds ratio surface over the region of interest to capture how dependence varies over the region.We illustrate with a dataset from the Cape Floristic Region of South Africa consisting of more than 600 species at more than 600 sites. We present the spatial distribution of odds ratios for pairs of species that are positively correlated and pairs that are negatively correlated under the joint species distribution model.The multivariate normal covariance matrix associated with a collection of species is only a device for creating dependence among species but it lacks interpretation. By considering odds ratios, the quantitative ecologist will be able to better appreciate the practical dependence between species that is implicit in these joint species distribution modeling specifications.

An Alternative Method for Identifying a Failed Engine in Twin-Engine Propeller Aircraft

2020 ◽  
Vol 10 (2) ◽  
pp. 103-111
Author(s):  
Andrey K. Babin ◽  
Andrew R. Dattel ◽  
Margaret F. Klemm
Keyword(s):  
Human Error ◽  
Flight Simulator ◽  
Traditional Group ◽  
Aircraft Accidents ◽  
Alternative Method ◽  
Indicator Group ◽  
Group A ◽  
The Dead ◽  
Engine Failure ◽  
And Performance

Abstract. Twin-engine propeller aircraft accidents occur due to mechanical reasons as well as human error, such as misidentifying a failed engine. This paper proposes a visual indicator as an alternative method to the dead leg–dead engine procedure to identify a failed engine. In total, 50 pilots without a multi-engine rating were randomly assigned to a traditional (dead leg–dead engine) or an alternative (visual indicator) group. Participants performed three takeoffs in a flight simulator with a simulated engine failure after rotation. Participants in the alternative group identified the failed engine faster than the traditional group. A visual indicator may improve pilot accuracy and performance during engine-out emergencies and is recommended as a possible alternative for twin-engine propeller aircraft.

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