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 685 for “"non-parametric"”.
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Non-Parametric Spatial Models
… play a central role in spatial statistics. Parametric covariance functions have been used in most of the existing works on the analysis of spatial data. The primary reason for this is that the classes of parametric covariance functions guarantee that the fitted covariance function is …
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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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SEQUENTIAL METHODS FOR NON-PARAMETRIC HYPOTHESIS TESTING
… we propose two algorithms for sequential non-parametric hypothesis testing. The proposed algorithms are based on the random distortion testing (RDT) framework. The RDT framework addresses the problem of testing whether a random signal observed in additive noise deviates by more than a …
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Non-parametric modelling of signals on graphs
… that Gaussian processes, a class of Bayesian non-parametric models, are particularly well suited for modelling data on graph domains. To provide evidence for this hypothesis, I demonstrate the merits of Bayesian non-parametric modelling for graph data by deriving Gaussian process models for …
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Representation discovery in non-parametric reinforcement learning
Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. …
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Essays on semi-/non-parametric methods in econometrics
… contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We …
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Non-parametric competing risks with multivariate frailty models
… which may or may not have a tractable form. The parametric competing risk model, in which it is assumed that the failure times are coming from a known distribution, is widely used such as Weibull, Gamma and other distributions. The Gamma distribution has been widely used as a frailty …
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Non-parametric bayesian methods for structured topic models
… structures. These models take advantage of non-parametric Bayesian techniques (e.g., the two-parameter Poisson-Dirichlet process (PDP)) and Markov chain Monte Carlo methods. Two preliminary contributions of this thesis are 1. The Compound Poisson-Dirichlet process (CPDP): it is an extension …
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Non-parametric bayesian methods for structured topic models
… structures. These models take advantage of non-parametric Bayesian techniques (e.g., the two-parameter Poisson-Dirichlet process (PDP)) and Markov chain Monte Carlo methods. Two preliminary contributions of this thesis are 1. The Compound Poisson-Dirichlet process (CPDP): it is an extension …
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Non-parametric Bayesian models for structured output prediction
… and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maintaining the advantages of Bayesian …
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Historically implied swaption skews using non-parametric methods
… this the dissertation adopts and constructs non-parametric methods which only make use of historical realised data of the underlying variable rather than any implied pricing history of the derivative itself. Stutzer's method of canonical valuation (1996) is adapted for use with interest rate …
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Non-parametric threshold for smoothed empirical Wasserstein distance
… 𝑑), P*𝒩 (0, 𝜎² 𝐼 subscript 𝑑)) converges at the parametric rate 𝑂(1/𝑛), and when 𝐾 > 𝜎, there exists a 𝐾-subgaussian distribution P such that 𝑊₂² (Pₙ *𝒩 (0, 𝜎² 𝐼 subscript 𝑑), P* 𝒩 (0, 𝜎² 𝐼 subscript 𝑑)) = 𝜔(1/𝑛). This resolves the open problems in[7], closes the gap between where we get …
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Non-Parametric Priors for Functional Data and Partition Labelling Models
<p>Previous papers introduced a variety of extensions of the Dirichlet process to the func-</p><p>tional domain, focusing on the challenges presented by extending the stick-breaking</p><p>process. In this thesis some of these are examined in more detail for similarities</p><p>and differences in …
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Non parametric Estimation of high-frequency Volatility and Correlation Dynamics
… of the Fourier estimator, a newly proposed nonparametric methodology to measure ex-post volatility and cross-volatilities as functions of time, when financial assets are observed at different highfrequency levels over the day. The estimator has the peculiar feature to employ the observed …
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Experimental evaluation of the efficiencies of certain non- parametric statistics
… be. The use of the 10% level of significance in non-parametric tests does seem unrealistic, because, in general, non-parametric statistics tend to be more conservative than parametric statistics. In case non-parametric methods are applied to samples from a population which is normally …
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Parametric, Non-Parametric And Statistical Modeling Of Stony Coral Reef Data
… from the jackknife procedure for the Shannon-Wiener diversity index used in previous studies. We investigate a new and more effective approach to estimating the Shannon-Wiener and Simpson's diversity index. In chapter four, we develop the best possible estimate of the probability …
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Evaluation of performance of non-parametric confidence intervals on skewed data
… will develop a method for the construction of a non-parametric confidence interval and compare it to parametric confidence intervals for the mean of a population distribution. Using Monte Carlo simulation, we will examine the performance of both confidence intervals on data from different types …
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Adaptation in Non-Parametric State Estimation with Application to People Tracking
The employment of visual sensor networks in surveillance systems has brought in as many challenges as disadvantages. While the integration of multiple cameras into a network has the potential advantage of fusing complementary observations from sensors and enlarging visual coverage, it also …
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Efficiency measurement. A methodological comparison of parametric and non-parametric approaches.
… frontier efficiency estimation techniques from parametric and non-parametric approaches. Five different frontier efficiency estimation techniques are considered which are SFA, DFA, DEA-CCR, DEA-BCC and DEA-RAM. These techniques are then used on an artificially generated panel dataset using a …
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