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41 
Some factors that effect [sic] statistical power in ANCOVA: a population studyTvedt, Valerie Maria 01 January 2000 (has links)
A study into the factors that affect power in an analysis of covariance (ANCOVA) design were examined. Four factors  sample size, significance level, dependent variablecovariate correlations and homogeneity of regression  were varied in a population study. Results indicate that power increased when the dependent variablecovariate correlations increased and when sample size increased. Power also increased when a less stringent alpha level was used. Homogeneity of regression did not effect power. Implications and recommendations for the applied researcher are discussed.

42 
Judgement poststratification for designed experimentsDu, Juan 07 August 2006 (has links)
No description available.

43 
Monte Carlo simulation with parametric and nonparametric analysis of covariance for nonequivalent control groupsBender, Mary January 1987 (has links)
There are many parametric statistical models that have been designed to measure change in nonequivalent control group studies, but because of assumption violations and potential artifacts, there is no one form of analysis that always appears to be appropriate. While the parametric analysis of covariance and parametric ANCOVAS with a covariate correction are some of the more frequently completed analyses used in nonequivalent control group research, comparative studies with nonparametric counterparts should be completed and results compared with those more commonly used forms of analysis.
The current investigation studied and compared the application of four ANCOVA models: the parametric, the covariatecorrected parametric, the rank transform, and the covariatecorrected rank transform. Population parameters were established; sample parameter intervals determined by Monte Carlo simulation were examined; and a best ANCOVA model was systematically and theoretically determined in light of population assumption violations, reliability of the covariate correction, the width of the sample probability level intervals, true parent population parameters, and results of robust regression.
Results of data exploration on the parent population revealed that, based on assumptions, the covariatecorrected ANCOVAS are preferred over both the parametric and rank analyses. A reliability coefficient of ṟ=.83 also indicated that a covariatecorrected ANCOVA is effective in error reduction. Robust regression indicated that the outliers in the data set impacted the regression equation for both parametric models, and deemed selection of either model questionable.
The tightest probability level interval for the samples serves to delineate the model with the greatest convergence of probability levels, and, theoretically, the most stable model. Results of the study indicated that, because the covariatecorrected rank ANCOVA had by far the tightest interval, it is the preferred model. In addition, the probability level interval of the covariatecorrected rank model is the only model interval that contained the true population parameter.
Results of the investigation clearly indicate that the covariatecorrected rank ANCOVA is the model of choice for this nonequivalent control group study. While its use has yet to be reported in the literature, the covariatecorrected rank analysis of covariance provides a viable alternative for researchers who must rely upon intact groups for the answers to their research questions. / Ph. D.

44 
Thinning of point processescovariance analysesChandramohan, Jagadeesh January 1982 (has links)
This dissertation addresses a class of problems in point process theory called 'thinning'. By thinning we mean an operation whereby a point process is split into two point processes by some rule. We obtain the covariance structure between the thinned processes under various thinning rules. We first obtain this structure for independent Bernoulli thinning of an arbitrary point process. We show that if the point process is a renewal (stationary or ordinary) process, the thinned processes will be uncorrelated if and only if the renewal process is Poisson in which case the thinned processes are independent. Thus, we have a situation where zero correlation implies independence. We also show that while the intervals between events in the thinned processes may be uncorrelated, the counts need not be.
Next, we obtain the covariance structure between the thinned processes resulting from a mark dependent thinning of a Markov renewal process with a Polish mark space. These results are used to study the overflow queue where we show that in equilibrium the input and overflow processes are uncorrelated as are the output and overflow processes. We thus provide an example where two uncorrelated but dependent renewal processes, neither of which is Poisson but which produce a Poisson process when superposed.
Next, we study Bernoulli thinning of an alternating Markov process and show that the thinned process are uncorrelated if and only if the process is Poisson in which case the thinned processes are independent. Finally, we obtain the covariance structure obtained when a renewal process is thinned by an independent Markov chain. We show that if the renewal process is Poisson and the chain is stationary, the thinned processes will be uncorrelated if and only if the Markov chain is a Bernoulli process. We also show how to design the chain to obtain a prespecified covariance function.
We then close the dissertation by summarizing the work presented and indicating areas of further research including a short discussion of the use of Laplace functionals in the determination of joint distributions of thinned processes. / Ph. D.

45 
Missing values in covariance in the case of the randomized blockShannon, Catherine January 1948 (has links)
The formula and theory for estimating a missing value in the case of covariance in a randomized block has been presented in this paper. It has also been found that the formula given corresponds to Yates’ formula for a missing value in a randomized block when there is only one variable present in the experiment. / Master of Science

46 
A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese charactersWang, Yongqiang, 王永強 January 2009 (has links)
published_or_final_version / Computer Science / Master / Master of Philosophy

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Evaluation of motion compensated ADV measurements for quantifying velocity fluctuationsUnknown Date (has links)
This study assesses the viability of using a towfish mounted ADV for quantifying water velocity fluctuations in the Florida Current relevant to ocean current turbine performance. For this study a motion compensated ADV is operated in a test flume. Water velocity fluctuations are generated by a 1.3 cm pipe suspended in front of the ADV at relative current speeds of 0.9 m/s and 0.15 m/s, giving Reynolds numbers on the order of 1000. ADV pitching motion of +/ 2.5 [degree] at 0.3 Hz and a heave motion of 0.3 m amplitude at 0.2 Hz are utilized to evaluate the motion compensation approach. The results show correction for motion provides up to an order of magnitude reduction in turbulent kinetic energy at frequencies of motion while the IMU is found to generate 2% error at 1/30 Hz and 9% error at 1/60 Hz in turbulence intensity. / by James William Lovenbury. / Thesis (M.S.C.S.)Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

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Improved estimation of the scale matrix in a onesample and twosample problem.January 1998 (has links)
by FoonYip Ng. / Thesis (M.Phil.)Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 111115). / Abstract also in Chinese. / Chapter Chapter 1  Introduction  p.1 / Chapter 1.1  Main Problems  p.1 / Chapter 1.2  The Basic Concept of Decision Theory  p.4 / Chapter 1.3  The Class of Orthogonally Invariant Estimators  p.6 / Chapter 1.4  Related Works  p.8 / Chapter 1.5  Summary  p.10 / Chapter Chapter 2  Estimation of the Scale Matrix in a Wishart Distribution  p.12 / Chapter 2.1  Review of the Previous Works  p.13 / Chapter 2.2  Some Useful Statistical and Mathematical Results  p.15 / Chapter 2.3  Improved Estimation of Σ under the Loss L1  p.18 / Chapter 2.4  Simulation Study for Wishart Distribution under the Loss L1  p.22 / Chapter 2.5  Improved Estimation of Σ under the Loss L2  p.25 / Chapter 2.6  Simulation Study for Wishart Distribution under the Loss L2  p.28 / Chapter Chapter 3  Estimation of the Scale Matrix in a Multivariate F Distribution  p.31 / Chapter 3.1  Review of the Previous Works  p.32 / Chapter 3.2  Some Useful Statistical and Mathematical Results  p.35 / Chapter 3.3  Improved Estimation of Δ under the Loss L1____  p.38 / Chapter 3.4  Simulation Study for Multivariate F Distribution under the Loss L1  p.42 / Chapter 3.5  Improved Estimation of Δ under the Loss L2 ________  p.46 / Chapter 3.6  Relationship between Wishart Distribution and Multivariate F Distribution  p.51 / Chapter 3.7  Simulation Study for Multivariate F Distribution under the Loss L2  p.52 / Chapter Chapter 4  Estimation of the Scale Matrix in an Elliptically Contoured Matrix Distribution  p.57 / Chapter 4.1  Some Properties of Elliptically Contoured Matrix Distributions  p.58 / Chapter 4.2  Review of the Previous Works  p.60 / Chapter 4.3  Some Useful Statistical and Mathematical Results  p.62 / Chapter 4.4  Improved Estimation of Σ under the Loss L3  p.63 / Chapter 4.5  Simulation Study for MultivariateElliptical t Distributions under the Loss L3  p.67 / Chapter 4.5.1  Properties of MultivariateElliptical t Distribution  p.67 / Chapter 4.5.2  Simulation Study for Multivariate Elliptical t Distributions  p.70 / Chapter 4.6  Simulation Study for εContaminated Normal Distributions under the Loss L3  p.74 / Chapter 4.6.1  Properties of εContaminated Normal Distributions  p.74 / Chapter 4.6.2  Simulation Study for 2Contaminated Normal Distributions  p.76 / Chapter 4.7  Discussions  p.79 / APPENDIX  p.81 / BIBLIOGRAPHY  p.111

49 
Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment NetworksWan, Phyllis January 2018 (has links)
This thesis covers four topics: i) Measuring dependence in time series through distance covariance; ii) Testing goodnessoffit of time series models; iii) Threshold selection for multivariate heavytailed data; and iv) Inference for linear preferential attachment networks.
Topic i) studies a dependence measure based on characteristic functions, called distance covariance, in time series settings. Distance covariance recently gathered popularity for its ability to detect nonlinear dependence. In particular, we characterize a general family of such dependence measures and use them to measure lagged serial and cross dependence in stationary time series. Assuming strong mixing, we establish the relevant asymptotic theory for the sample auto and cross distance correlation functions.
Topic ii) proposes a goodnessoffit test for general classes of time series model by applying the autodistance covariance function (ADCV) to the fitted residuals. Under the correct model assumption, the limit distribution for the ADCV of the residuals differs from that of an i.i.d. sequence by a correction term. This adjustment has essentially the same form regardless of the model specification.
Topic iii) considers data in the multivariate regular varying setting where the radial part $R$ is asymptotically independent of the angular part $\Theta$ as $R$ goes to infinity. The goal is to estimate the limiting distribution of $\Theta$ given $R\to\infty$, which characterizes the tail dependence of the data. A typical strategy is to look at the angular components of the data for which the radial parts exceed some threshold. We propose an algorithm to select the threshold based on distance covariance statistics and a subsampling scheme.
Topic iv) investigates inference questions related to the linear preferential attachment model for network data. Preferential attachment is an appealing mechanism based on the intuition “the rich get richer” and produces the wellobserved powerlaw behavior in net works. We provide methods for fitting such a model under two data scenarios, when the network formation is given, and when only a singletime snapshot of the network is observed.

50 
Estimation of the scale matrix and their eigenvalues in the Wishart and the multivariate F distribution.January 1996 (has links)
by WaiYin Chan. / Thesis (M.Phil.)Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 4245). / Chapter Chapter 1  Introduction / Chapter 1.1  Main Problems  p.1 / Chapter 1.2  Class of Regularized Estimator  p.4 / Chapter 1.3  Preliminaries  p.6 / Chapter 1.4  Related Works  p.9 / Chapter 1.5  Brief Summary  p.10 / Chapter Chapter 2  Estimation of the Covariance Matrix and its Eigenvalues in a Wishart Distribution / Chapter 2.1  Significance of The Problem  p.12 / Chapter 2.2  Review of the Previous Work  p.13 / Chapter 2.3  Properties of the Wishart Distribution  p.18 / Chapter 2.4  Main Results  p.20 / Chapter 2.5  Simulation Study  p.23 / Chapter Chapter 3  Estimation of the Scale Matrix and its Eigenvalues in a Multivariate F Distribution / Chapter 3.1  Formulation and significance of the Problem  p.26 / Chapter 3.2  Review of the Previous Works  p.28 / Chapter 3.3  Properties of Multivariate F Distribution  p.30 / Chapter 3.4  Main Results  p.33 / Chapter 3.5  Simulation Study  p.38 / Chapter Chapter 4  Further research  p.40 / Reference  p.42 / Appendix  p.46

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