Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 36 for “"Nonparametric statistics"”.
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Contributions to asymptotic theory in nonparametric statistics
Nonparametric statistics, in which the parameter of interest is allowed to be infinite-dimensional, provides a theoretical lens through which to analyse the performance of modern data science and machine learning methods. The need for a rigorous understanding of these procedures has never been …
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The use of nonnull models for ranks in nonparametric statistics
"A popular nonparametric measure of a monotone relation between two variables is Kendall's tau. Originally, most analysis of this statistic assumed the two variables were independent, while more recently there has been interest in finding ""nonnull"" models for the observations which allow types of …
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Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics
Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying …
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An exploration of parametric versus nonparametric statistics in occupational therapy clinical research
… necessary for appropriate use of parametric statistics (Lezak and Gray, 1984). When assumptions underlying the use of the parametric tests are violated or cannot be documented, the power of the parametric test may be invalidated and consequently, the significance levels inaccurate (Gibbons, …
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The curve through the expected values of order statistics with special reference to problems in nonparametric tests of hypotheses
… literature and especially in the theory of nonparametric statistics. A new expression for this value will be obtained tor any underlying density f(x) but emphasis will be placed on normal scores. A finite series representation, the individual terms of which are easy to calculate, will be …
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Evaluation of Using the Bootstrap Procedure to Estimate the Population Variance
<p>The bootstrap procedure is widely used in nonparametric statistics to generate an empirical sampling distribution from a given sample data set for a statistic of interest. Generally, the results are good for location parameters such as population mean, median, and even for estimating a …
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An evaluation of a technical assistance program for special education area cooperatives in Kentucky
… of the data included both descriptive and nonparametric statistics. While statistical significant was not shown, the analysis indicated the program was successful in strengthening the cooperative concept.
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The algebraic statistics of sampling, likelihood, and regression
… models and algebraic varieties. Algebraic Statistics unites these two concepts, turning algebraic structure into statistical insight. Featured here are three types of models that have such an algebraic structure. Linear Gaussian covariance models are continuous models which are simple to …
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A comparison of decontextualized and contextualized reading skills in persons with severe aphasia
… condition were compared with descriptive and nonparametric statistics. Results showed significant improvement in reading comprehension accuracy when the CCRC method was applied. Clinically, this suggests that persons with severe aphasia can read well enough to use partner supported …
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Bayesian nonparametric approaches for reinforcement learning in partially observable domains
… of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. We show how the representations learned using Bayesian nonparametric methods result in better …
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On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models
… variance variations is obtained. The methods of nonparametric statistics are used to determine the success of the modeling of nonstationarity. Justification of the method is obtained through comparison of rainflow cycle distributions and resulting fatigue lives of original and simulated loadings. …
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Multivariate nonparametric control charts using small samples
… a multivariate control chart based on classical nonparametric statistics could be used. Control charts based on the sign and signed rank statistics are explored. Past sample information for each variable is retained through an exponentially weighted moving average statistic (EWMA) in order to …
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Geometry and Optimization of Relative Arbitrage
… reveals deep connections with optimal transport, nonparametric statistics and information geometry. Our main object of study is functionally generated portfolio, a family of volatility harvesting investment strategies with remarkable properties. This thesis consists of three parts. Part I gives a …
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A Study of Instructional Scheduling, Teaming, and Common Planning in New York State Middle Schools
… analyzed using descriptive, inferential, and nonparametric statistics. In addition, demographic characteristics and principals’ beliefs regarding these three school supports were also analyzed.</p> <p>The results indicated that the majority of principals utilize a traditional departmentalized …
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Decision making in the presence of complex dynamics from limited, batch data
… how this can be accomplished by using Bayesian nonparametric statistics to model the dynamics, which can then be used for planning. We then present an alternative approach which grows the policy class using the principle of structural risk minimization, for which the resulting algorithm has …
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Measuring Safety Attitude Differences in the Construction Supply Chain
… Confirmatory Factor Analysis, and using applied nonparametric statistics to analyze the data and identify significant differences. These results will be used in Phase II to develop a training tool to educate relevant project personnel on differences that were identified in Phase I, and to …
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Optimization Methods for Machine Learning under Structural Constraints
… with a focus on shape constraints in nonparametric statistics and sparsity in high-dimensional statistics. In the first chapter, we consider the subgradient regularized convex regression problem, which aims to fit a convex function between the target variable and covariates. We propose …
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Some results in non-parametric calibration
In statistical terminology, calibration is the problem of recovering the value of the control variable when the value of the response is given. Mathematically, it is the problem of determining the values ofthe independent variables that respond to a given function value: in other words, the problem …
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The Evolution of the El Niño-Southern Oscillation and Tropical Pacific Climate Across the Last Deglaciation
… of hypothetical ENSO change and robust, nonparametric statistics to reconstruct “snapshots” of past ENSO activity from the Holocene, the Last Glacial Maximum (LGM), and the abrupt climate events of the last deglaciation. Taken together, the results indicate that Modern/Late Holocene ENSO …
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Modern k-Nearest Neighbour Methods in Entropy Estimation, Independence Testing and Classification
… neighbour methods are a classical approach in nonparametric statistics. The k-nearest neighbour classifier can be traced back to the seminal work of Fix and Hodges (1951) and they also enjoy popularity in many other problems including density estimation and regression. In this thesis we study …
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