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 651 for “"autoregressive"”.
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Bandits in autoregressive Markov models
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Tests for fourth order autoregressive processes.
… This statistic is able to detect a fourth order autoregressive disturbance of the form: Ɛ(ʈ)=ƿ(1)Ɛ(ʈ-1)+ƿ(4)Ɛ(ʈ-4)+ƞ(ʈ) in the general model Y=Xβ+Ɛ. To correct for this disturbance and thus yield efficient regression estimates, a data transformation was derived using the inverse of the …
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Equivariant Autoregressive Models for Molecular Generation
… we have developed Symphony, an E(3)-equivariant autoregressive generative model for 3D structures of small molecules. At each sampling iteration, a single focus atom is selected, which is then used to decide on the next atom’s position within its neighborhood. Symphony built on previous …
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Divide and recombine: Autoregressive models and STL+
<p>In this thesis multiple methods are proposed and applied to the Akamai CIDR time series data. The Akamai network is one of the world's largest distributed-computing platforms, with more than 250,000 servers in more than 80 countries. It is responsible for 15-20 percent of all web traffic. We …
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The bootstrap approach to autoregressive time series analysis
Please read the abstract in the section 00front of this document Accompanied by 1 disc available at main counter with shelf number EM 519.5 DE KOSTER. Master copy at back of book
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Autoregressive hidden Markov models and the speech signal
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. This new variant of the HMM is built upon the mathematical structure of the HMM and linear prediction analysis of speech signals. By incorporating these two methods into one …
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SAMoSSA: Multivariate Singular Spectrum Analysiswith Stochastic Autoregressive Noise
… of deterministic non-stationary components, the Autoregressive (AR) stationary component can also be learnt readily, e.g. via Ordinary Least Squares (OLS). However, a theoretical underpinning of multi-stage learning algorithms involving both deterministic and stationary components has been absent …
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The autoregressive stochastic block model with changes in structure
… block model is presented which includes autoregressive terms. This novel approach to dynamic network models allows the present state of an edge to influence future states, and is therefore named the autoregresssive stochastic block model. Furthermore, an algorithm to perform inference on …
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Internal multiscale autoregressive processes, stochastic realization, and covariance extension
… thesis is on the identification of multiscale autoregressive (MAR) models for stochastic processes from second-order statistical characterizations. The class of MAR processes constitutes a rich and powerful stochastic modeling framework that admits efficient statistical inference algorithms. To …
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A multivariate autoregressive model for limited area weather forecasting
… of the implementation of the multivariate autoregressive research model. This includes a FORTRAN program and three order selection routines. We conclude with a discussion on possible extensions of our results and other applications.
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A MULTI-COUNTRY LINK VECTOR AUTOREGRESSIVE MODEL THEORY AND EVIDENCE
… the construction of a multi-country Link Vector Autoregressive (LIN-VAR) model based on eighteen OECD economies. It offers a unique way of combining the linkage specifications of global structural econometric models with the VAR technique. Each national economy in the LINK-VAR model is specified …
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Three Essays on the Spatial Autoregressive Model in Spatial Econometrics
The spatial autoregressive model (SAR) is a standard tool to analyze spatial data. It is of great interest in econometrics because it has a game structure and, therefore, can be interpreted as a reaction function: the outcome or behavior of observations at one location is directly affected by those …
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System Simulation Output Analysis by Autoregressive-Integrated Moving Average Models
Made available in DSpace on 2015-05-12T22:38:11Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 7309930.PDF: 5142666 bytes, checksum: 5ae56d3bb78e2c7de46581dcc2c2857c (MD5) Previous issue date: 1972
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Multistage mean-variance portfolio selection in cointegrated vector autoregressive systems
… linear system known as a cointegrated vector autoregressive (VAR) process. While MVO portfolio choice remains the most popular formulation for single-stage asset allocation problems in both academia and industry, computational difficulties traditionally limit its use in a dynamic, multistage …
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
… data. In particular, intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data corresponding to spatial partitions of a region. However, selection of prior distributions for these spatial parameters …
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Testing for Constancy of Correlation in Autoregressive Conditional Heteroscedasticity (Arch) Models
… constant correlation multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models (Bollerslev (1990)). Bollerslev (1990) states ""... the validity of the model remains an empirical question"". We show that the test statistic derived in the unconditional normal case can …
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An empirical comparison of autoregressive and rational models of price expectations
… inflation expectations process are tested: an autoregressive model which uses only past inflation data; a rational expectations model which utilizes the structural economic relationships in the economy (excluding past inflation); and a general model which exploits both the information sets just …
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Three essays on bias, bias reduction and estimation in autoregressive time series models
… consists of three essays on the subject of autoregressive time series of order one. The first essay derives an approximate bias of the ordinary least squares estimator (OLS) of the autoregressive parameter for series with moderate deviations from a unit root and for a fixed autoregressive …
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On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models
… by a class of time series referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is used to account for the variation of the mean and variance. Due to the use of random phase angles in the Fourier series, an ensemble of mean and variance variations is obtained. …
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Inferring Biological Networks from Time-Course Observations Using a Non-linear Vector Autoregressive Model
Cellular functions are increasingly viewed as being regulated through networks of molecules working in parallel. Discovery and characterization of these networks is fundamental to understanding diseases and developing treatments. In this work we present a method for inferring biological networks …
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