ESTIMATION OF THE OFFSPRING DISTRIBUTION AND THE MEAN VECTOR FOR A BISEXUAL GALTON-WATSON PROCESS

2001 ◽  
Vol 30 (3) ◽  
pp. 497-516 ◽  
Author(s):  
M. González ◽  
M. Molina ◽  
M. Mota
Keyword(s):  
Offspring Distribution ◽  
The Mean ◽  
Watson Process ◽  
Mean Vector

On the non-existence of consistent estimates in Galton-Watson processes

1982 ◽  
Vol 19 (4) ◽  
pp. 842-846 ◽  
Author(s):  
Richard Lockhart
Keyword(s):  
Offspring Distribution ◽  
Lattice Size ◽  
Single Realization ◽  
The Mean ◽  
Watson Process ◽  
Mean Variance

Estimation of the offspring distribution from a single realization of a supercritical Galton-Watson process is studied. It is shown that, based on population totals, a parameter of the offspring distribution cannot be estimated unless it is determined by the mean, variance, lattice size and lattice offset of the offspring distribution.

On the non-existence of consistent estimates in Galton-Watson processes

1982 ◽  
Vol 19 (04) ◽  
pp. 842-846
Author(s):  
Richard Lockhart
Keyword(s):  
Offspring Distribution ◽  
Lattice Size ◽  
Single Realization ◽  
The Mean ◽  
Watson Process ◽  
Mean Variance

Estimation of the offspring distribution from a single realization of a supercritical Galton-Watson process is studied. It is shown that, based on population totals, a parameter of the offspring distribution cannot be estimated unless it is determined by the mean, variance, lattice size and lattice offset of the offspring distribution.

Tests for the mean vector under intraclass covariance structure

1981 ◽  
Vol 12 (3-4) ◽  
pp. 237-245 ◽  
Author(s):  
Bernard Clement ◽  
Sukharanyan Chakraborty ◽  
Bimal K. Sinha ◽  
Narayan C. Giri
Keyword(s):  
Covariance Structure ◽  
The Mean ◽  
Mean Vector

scholarly journals BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO

2018 ◽  
Vol 21 (08) ◽  
pp. 1850054 ◽  
Author(s):  
DAVID BAUDER ◽  
TARAS BODNAR ◽  
STEPAN MAZUR ◽  
YAREMA OKHRIN
Keyword(s):  
Bayesian Inference ◽  
Point Of View ◽  
Posterior Distributions ◽  
Conditional Distributions ◽  
Linear Combinations ◽  
Conjugate Priors ◽  
Mean And Variance ◽  
The Mean ◽  
Stochastic Representations ◽  
Mean Vector

In this paper, we consider the estimation of the weights of tangent portfolios from the Bayesian point of view assuming normal conditional distributions of the logarithmic returns. For diffuse and conjugate priors for the mean vector and the covariance matrix, we derive stochastic representations for the posterior distributions of the weights of tangent portfolio and their linear combinations. Separately, we provide the mean and variance of the posterior distributions, which are of key importance for portfolio selection. The analytic results are evaluated within a simulation study, where the precision of coverage intervals is assessed.

Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Wei Zhao ◽  
Wei Hou ◽  
Ramon C. Littell ◽  
Rongling Wu
Keyword(s):  
Genetic Correlations ◽  
Covariance Matrices ◽  
Growth Trajectories ◽  
Relative Importance ◽  
Hybrid Progeny ◽  
Linkage Groups ◽  
Stem Height ◽  
Pleiotropic Qtl ◽  
The Mean ◽  
Mean Vector

In this article, we present a statistical model for mapping quantitative trait loci (QTL) that determine growth trajectories of two correlated traits during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by mathematical aspects of growth processes to model the mean vector and by structured antedependence (SAD) models to approximate time-dependent covariance matrices for longitudinal traits. It provides a quantitative framework for testing the relative importance of two mechanisms, pleiotropy and linkage, in contributing to genetic correlations during ontogeny. This model has been employed to map QTL affecting stem height and diameter growth trajectories in an interspecific hybrid progeny of Populus, leading to the successful discovery of three pleiotropic QTL on different linkage groups. The implications of this model for genetic mapping within a broader context are discussed.

On Sequential Procedures for the Point Estimation of the Mean Vector

1988 ◽  
Vol 37 (1-2) ◽  
pp. 47-54 ◽  
Author(s):  
R. Karan Singh ◽  
Ajit Chaturvedi
Keyword(s):  
Normal Population ◽  
Stopping Time ◽  
Point Estimation ◽  
Multivariate Normal ◽  
Minimum Risk ◽  
Sequential Procedures ◽  
Multivariate Normal Population ◽  
Bounded Risk ◽  
The Mean ◽  
Mean Vector

Sequential procedures are proposed for (a) the minimum risk point estimation and (b) the bounded risk point estimation of the mean vector of a multivariate normal population . Second-order approximations are derived. For the problem (b), a lower bound for the number of additional observations (after stopping time) is obtained which ensures “ exact” boundedness of the risk associated witb the sequential procedure.

On the parity of individuals in a branching process

1976 ◽  
Vol 13 (02) ◽  
pp. 219-230 ◽  
Author(s):  
J. Gani ◽  
I. W. Saunders
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Branching Process ◽  
Yeast Cells ◽  
Death Process ◽  
Second Moments ◽  
The Mean ◽  
Watson Process ◽  
Number Of Cells ◽  
Birth Death

This paper is concerned with the parity of a population of yeast cells, each of which may bud, not bud or die. Two multitype models are considered: a Galton-Watson process in discrete time, and its analogous birth-death process in continuous time. The mean number of cells with parity 0, 1, 2, … is obtained in both cases; some simple results are also derived for the second moments of the two processes.

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