A note on a manova model applied to problems in growth curve

Khatri, C. G.

doi:10.1007/bf02869518

Cited by 340 publications

(119 citation statements)

References 10 publications

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“…Khatri (1966) extended this result and showed that the MLE also is a weighted estimator. Under a certain covariance structure, Rao (1967) and Reinsel (1982) have shown that the unweighted estimator also is the MLE.…”

Section: Introductionmentioning

confidence: 79%

Test for the mean matrix in a Growth Curve model for high dimensions

Srivastava

Singull

2016

Communications in Statistics - Theory and Methods

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In this paper, we consider the problem of estimating and testing a general linear hypothesis in a general multivariate linear model, the so called Growth Curve model, when the p × N observation matrix is normally distributed with an unknown covariance matrix.The maximum likelihood estimator (MLE) for the mean is a weighted estimator with the inverse of the sample covariance matrix which is unstable for large p close to N and singular for p larger than N . We modify the MLE to an unweighted estimator and propose a new test which we compare with the previous likelihood ratio test (LRT) based on the weighted estimator, i.e., the MLE. We show that the performance of this new test based on the unweighted estimator is better than the LRT based on the MLE.For the high-dimensional case, when p is larger than N , we construct two new tests based on the trace of the variation matrices due to the hypothesis (between sum of squares) and the error (within sum of squares).To compare the performance of all four tests we compute the attained significance level and the empirical power.

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Section: Introductionmentioning

confidence: 79%

Test for the mean matrix in a Growth Curve model for high dimensions

Srivastava

Singull

2016

Communications in Statistics - Theory and Methods

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“…We shall think of each column as being an independent copy of (τ, C) ⊤ from a regenerative cycle. To test the hypothesis H 0 : θξ 1 − ξ 2 = 0, we compute the quantities where C = [θ, −1] and A is an 1 × n-vector of ones; see Khatri (1966) or Srivastava and Carter (1983, Section 6.3.2). These expressions follow from Khatri (1966, Eqs.…”

Section: A the Fieller Test As A Manova Testmentioning

confidence: 99%

A Note on Skewness in Regenerative Simulation

Asmussen

Rydén

2010

Communications in Statistics - Simulation and Computation

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“…Such correlated data over time, labeled 'growth curves', can be analysed using the methods introduced in Potthoff and Roy [12]. It has been discussed and analysed later by several authors, significant among them being Khatri [7], Grizzle and Allen [4], Rao [13], Pan and Fang [11] and Sengupta and Jammalamadaka [14]. Using specialized packages, one can also do exact tests for growth curve models, details of which can be found in Weerahandi [15].…”

Section: Growth Curve Modelingmentioning

confidence: 99%

Analysis of microtubule dynamics using growth curve models

Jammalamadaka

Siddiqi

Ghosh

2009

Journal of Applied Statistics

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Microtubules are part of the structural network within a cell's cytoplasm, providing structural support as well as taking part in many of the cellular processes. A large body of data provide evidence that dynamics of microtubules in a cell is reponsible for the performance of many critical cellular functions such as cell division. In this article, we study the effect of four different isoforms of a protein tau on microtubule dynamics using growth curve models. The results show that a linear growth curve model is sufficient to explain the data. Moreover, we find that a mutated version of a 3-repeat tau protein has a similar effect as a 4-repeat tau protein on microtubule dynamics. The latter findings conform with the biological understanding of the effect of the protein tau on microtubule dynamics.growth curves, microtubule dynamics, polynomial regression, splines, MANOVA, Wilks' Lamda,

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A note on a manova model applied to problems in growth curve

Cited by 340 publications

References 10 publications

Test for the mean matrix in a Growth Curve model for high dimensions

Test for the mean matrix in a Growth Curve model for high dimensions

A Note on Skewness in Regenerative Simulation

Analysis of microtubule dynamics using growth curve models

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