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.
Results
Showing 1 to 20 of 684 for “"Nonparametric"”.
-
Nonparametric directional perception
… exploration is theoretically founded in Bayesian nonparametric models, which capture two key properties of the 3D sensing process of an artificial perception system: (1) the inherent sequential nature of data acquisition and (2) that the required model complexity grows with the amount of observed …
-
Data Driven Nonparametric Detection
… distributions being fully or partially known, nonparametric scenarios are not well understood yet. This thesis mainly explores nonparametric models with unknown underlying distributions as well as semi-parametric models as an intermediate step to solve nonparametric problems.</p> <p>One major …
-
Essays in Nonparametric Econometrics
… aspects of the computation and application of nonparametric methods in Econometrics. All three chapters are linked by the use of Quantile Regression ideas and techniques. The first chapter uses stochastic approximation techniques to develop an algorithm for recursive estimation of a general …
-
Nonparametric Bayesian behavior modeling
As autonomous robots are increasingly used in complex, dynamic environments, it is crucial that the dynamic elements are modeled accurately. However, it is often difficult to generate good models due to either a lack of domain understanding or the domain being intractably large. In many domains, …
-
Nonparametric Modelling for Directional Data
… skewness and tail behavior. In particular, nonparametric density estimation is flexible and has a generally satisfactory performance. We adapt nonparametric and semiparametric mixtures developed for conventional data, and modify it for univariate and multivariate directional observations. …
-
Three essays on nonparametric estimation
… can be viewed as a middle ground between fully nonparametric and fully parametric estimators. For example, typical constraints require the estimator to be \log-concave or, more generally, \rho-concave; (Koenker and Mizera, 2010). In cases in which the true population density satisfies the shape …
-
Nonparametric metamodeling for simulation optimization
… surface using sample data. In particular, nonparametric regression is proposed as a useful tool in the global optimization of a response surface. As the general simulation optimization problem is very difficult and requires expertise from a number of fields, there is a growing consensus in …
-
Nonparametric procedures for process control
Three nonparametric control chart procedures are developed. The procedures are designed to detect any shift in the median of a sequence of observations from a specified control value. The first two procedures require that groups of g ≥ 1 observations be made sequentially on the output of the …
-
Advanced Nonparametric Bayesian Functional Modeling
… analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM …
-
Nonparametric Anomaly Detection and Secure Communication
… kernel Hilbert space. These tests are nonparametric without exploiting the information about $p$ and $q$ and are universally applicable to arbitrary $p$ and $q$. Furthermore, these tests are shown to be statistically consistent under certain conditions on the parameters of the problems. …
-
Nonparametric Bayesian Modelling in Machine Learning
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced …
-
Nonparametric Predictive Inference for Multiple Comparisons
This thesis presents Nonparametric Predictive Inference (NPI) for several multiple comparisons problems. We introduce NPI for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive …
-
Nonparametric smoothing in extreme value theory
This work investigates the modelling of non-stationary sample extremes using a roughness penalty approach, in which smoothed natural cubic splines are fitted to the location and scale parameters of the generalized extreme value distribution and the distribution of the r largest order statistics. …
-
Assessing Multivariate Heritability through Nonparametric Methods
… estimate is extremely difficult. This study uses nonparametric methods, namely the randomization test and the bootstrap, to obtain both a measure of the extremity of the observed heritability and an assessment of the uncertainty.
-
Nonparametric Survival Analysis under Shape Restrictions
… associated with a parametric model, we resort to nonparametric methods for estimating a function. Although other nonparametric approaches, such as Kaplan-Meier, kernel-based, and roughness penalty methods, are popular tools for solving function estimation problems, they suffer from some …
-
Bayesian Inference in Nonparametric Logistic Regression
… log-odds (logit) of the probability is estimated nonparametrically, using generalized smoothing splines.
-
Nonparametric sparse learning of dynamical systems
… systems. In this dissertation, we develop a nonparametric approach to learning the system dynamics via transfer operators in reproducing kernel Hilbert spaces (RKHS). Compared with methods using fixed parametric structures, the proposed nonparametric representation does not require manually …
-
Multiagent planning with Bayesian nonparametric asymptotics
… the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has prevented …
-
Bayesian nonparametric reward learning from demonstration
… The proposed method, termed Bayesian nonparametric inverse reinforcement learning (BNIRL), uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. The simple rewards are interpreted …
-
Latent source models for nonparametric inference
… understanding of when, why, and how well these nonparametric inference methods work in terms of key problem-specific quantities relevant to practitioners. This thesis bridges the gap between theory and practice for these methods in the three specific case studies of time series classification, …
Page 1 of 35