Dissertations / Theses on the topic 'Time series regression'
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Clark, Allan Ernest. "Model selection-regression and time series applications." Master's thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/18422.
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In any statistical analysis the researcher is often faced with the challenging task of gleaning relevant information from a sample data set in order to answer questions about the area under investigation. Often the exact data generating process that governs any data set is unknown, indicating that we have to estimate the data generating process by using statistical methods. Regression analysis and time series analysis are two statistical techniques that can be used to undertake such an analysis. In practice researcher will propose one model or a group of competing models that attempts to explain the data being investigated. This process is known as model selection. Model selection techniques have been developed to aid researchers in finding a suitable approximation to the true data generating process. Methods have also been developed that attempt to distinguish between different competing models. Many of these techniques entail using an information criterion that estimates the "closeness" of a fitted model to the unknown data generating process. This study investigates the properties of Bozdogan's Information complexity measure (ICOMP) when undertaking time series and regression analysis. Model selection techniques have been developed for both time series and regression analysis. The regression analysis techniques however often provide unsatisfactory results due to poor experimental designs. Poor experimental design could induce collinearities causing parameter estimates to become unstable with large standard errors. Time series analysis utilizes lagged autocorrelation- and lagged partial autocorrelation coefficients in order to specify the lag structure of the model. In certain data sets this process is not informative in determining the order of an ARIMA model. ICOMP guards against collinearity by considering the interaction between the parameters being estimated in a model. This study investigates the properties of ICOMP when undertaking regression and time series analysis by means of a simulation study. Bibliography: pages 250-263.
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Wu, Ying-keh. "Empirical Bayes procedures in time series regression models." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/76089.
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In this dissertation empirical Bayes estimators for the coefficients in time series regression models are presented. Due to the uncontrollability of time series observations, explanatory variables in each stage do not remain unchanged. A generalization of the results of O'Bryan and Susarla is established and shown to be an extension of the results of Martz and Krutchkoff.
Alternatively, as the distribution function of sample observations is hard to obtain except asymptotically, the results of Griffin and Krutchkoff on empirical linear Bayes estimation are extended and then applied to estimating the coefficients in time series regression models. Comparisons between the performance of these two approaches are also made.
Finally, predictions in time series regression models using empirical Bayes estimators and empirical linear Bayes estimators are discussed.Ph. D.
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Kidzinski, Lukasz. "Inference for stationary functional time series: dimension reduction and regression." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209226.
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Les progrès continus dans les techniques du stockage et de la collection des données permettent d'observer et d'enregistrer des processus d’une façon presque continue. Des exemples incluent des données climatiques, des valeurs de transactions financières, des modèles des niveaux de pollution, etc. Pour analyser ces processus, nous avons besoin des outils statistiques appropriés. Une technique très connue est l'analyse de données fonctionnelles (ADF).L'objectif principal de ce projet de doctorat est d'analyser la dépendance temporelle de l’ADF. Cette dépendance se produit, par exemple, si les données sont constituées à partir d'un processus en temps continu qui a été découpé en segments, les jours par exemple. Nous sommes alors dans le cadre des séries temporelles fonctionnelles.
La première partie de la thèse concerne la régression linéaire fonctionnelle, une extension de la régression multivariée. Nous avons découvert une méthode, basé sur les données, pour choisir la dimension de l’estimateur. Contrairement aux résultats existants, cette méthode n’exige pas d'assomptions invérifiables.
Dans la deuxième partie, on analyse les modèles linéaires fonctionnels dynamiques (MLFD), afin d'étendre les modèles linéaires, déjà reconnu, dans un cadre de la dépendance temporelle. Nous obtenons des estimateurs et des tests statistiques par des méthodes d’analyse harmonique. Nous nous inspirons par des idées de Brillinger qui a étudié ces models dans un contexte d’espaces vectoriels.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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余瑞心 and Sui-sum Amy Yu. "Application of Markov regression models in non-Gaussian time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31976840.
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Yan, Ka-lok, and 忻嘉樂. "Time series regression modelling of air quality data in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31252990.
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Dehoky, Dylan, and Edward Sikorski. "Understanding and Exploiting commodity currencies : A Study using time series Regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210167.
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This thesis within Industrial Economics and Applied Mathematics examines the term commodity currency. The thesis delves into analysing the characteristics and consequences of such a currency through a macroeconomic perspective while discussing previous studies within the matter. The applied mathematical statistics section audits the correlation between the currency and the commodities of the exporting country through a time series regression. The regression is based on the currency as the dependent variable and the commodities represent the covariates. Furthermore, a trading strategy is developed to see if a profit can be made on the foreign exchange market when looking at the commodity price movements.Det här kandidatexamensarbetet är skrivet inom industriell ekonomi och tillämpad matematik och granskar termen råvaruvaluta (commodity currency). Uppsatsen analyserar, utifrån ett makroekonomiskt perspektiv, karaktärsdragen och konsekvenserna av en sådan valuta, samtidigt som den diskuterar tidigare studier inom ämnet. Delen inom tillämpad matematik undersöker korrelationen mellan valutan och råvarorna som landet exporterar genom en tidsserieregression. Regressionen är baserad på valutan som responsvariabel samtidigt som råvarorna representerar kovariaterna. Den färdiga modellen används sedan i en handelsstrategi som försöker förutspå växelkursens rörelser genom att titta på råvarornas rörelser.
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Herath, Herath Mudiyanselage Wiranthe Bandara. "TENSOR REGRESSION AND TENSOR TIME SERIES ANALYSES FOR HIGH DIMENSIONAL DATA." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2585.
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Many real data are naturally represented as a multidimensional array called a tensor. In classical regression and time series models, the predictors and covariate variables are considered as a vector. However, due to high dimensionality of predictor variables, these types of models are inefficient for analyzing multidimensional data. In contrast, tensor structured models use predictors and covariate variables in a tensor format. Tensor regression and tensor time series models can reduce high dimensional data to a low dimensional framework and lead to efficient estimation and prediction. In this thesis, we discuss the modeling and estimation procedures for both tensor regression models and tensor time series models. The results of simulation studies and a numerical analysis are provided.
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Edlund, Per-Olov. "Preliminary estimation of transfer function weights : a two-step regression approach." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1989. http://www.hhs.se/efi/summary/291.htm.
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Maharesi, Retno. "Modelling time series using time varying coefficient autoregressive models : with application to several data sets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1994. https://ro.ecu.edu.au/theses/1099.
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In this thesis the state space approach and the Kalman recursions are used for modelling univariate time series data. The models that are examined in this thesis are time varying Coefficient Autoregressive models, which can be represented in state space form. The coefficients are assumed to change according to a stationary process, a non-stationary process or a random process. In order to be able to estimate these changing unknown coefficients, they will be treated as state variables and the equation describing the changes of the state variables will be given by the state equation. The model can then be expressed in the form of a measurement equation. The parameters of the model, which include the transition matrix T, the covariance matrices of the random terms in the state equation and the measurement equation denoted respectively by Q and R will be obtained using the EM algorithm developed by Shumway and Stoffer (1982). Other models considered for comparison in this thesis are the Box-Jenkins and Harvey's Structural models. The results of model fitting are illustrated by applying these three models to three special data sets. These results are compared to investigate whether the time varying coefficients model can provide a better fit, and, where appropriate, a suitable data -transformation is applied to the data sets in order to get a fit of the time varying coefficient autoregressive model.
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Hyung, Namwon. "Essays on panel and nonlinear time series analysis /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9958858.
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Liu, Xiang. "A Multi-Indexed Logistic Model for Time Series." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3140.
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In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare their performance using forecast accuracy and an area under the curve score via simulated sine waves with various intensities of Gaussian noise and Standard & Poors 500 historical data. Overall, that MILR outperforms SLR is validated on both realistic and simulated data. Finally, some possible future directions of research are discussed.
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Rodrigues, Antonio Jose Lopes. "Dynamic regression and supervised learning methods in time series modelling and forecasting." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364365.
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Rea, William Stanley. "The Application of Atheoretical Regression Trees to Problems in Time Series Analysis." Thesis, University of Canterbury. Mathematics and Statistics, 2008. http://hdl.handle.net/10092/1715.
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This thesis applies Atheoretical Regression Trees (ART) to the
problem of locating changes in mean in a time series where the
number and location of those changes are unknown. We undertook
an extensive simulation study into ART's performance on a range
of time series. We found ART to be a useful addition to currently
established structural break methodologies such as the CUSUM and
that due to Bai and Perron. ART was found to be useful in the
analysis of long time series which are not practical to analyze
with the optimal procedure of Bai and Perron.
ART was applied to a long standing problem in the analysis of
long memory time series.
We propose two new methods based on ART
for distinguishing between true long memory
and spurious long memory due to structural breaks. These methods
are fundamentally different from current tests and procedures
intended to discriminate between the two sets of competing
models.
The methods were
subjected to a simulation study and shown to be effective in
discrimination between simple regime switching models and
fractionally integrated processes.
We applied the new methods to 16 realized volatility series and
concluded they were not fractionally integrated series. All 16
series had mean shifts, some of which could be identified with
historical events.
We applied the new methods to a range of geophysical time series
and concluded they were not fractional Gaussian noises. All
of the series examined had mean shifts, some of which could
be identified with known climatic changes.
We conclude that our new methods are a significant advance in
model discrimination in long memory series.
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Arai, Yoichi. "Nonlinear nonstationary time series analysis and its application /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3144311.
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Strikholm, Birgit. "Essays on nonlinear time series modelling och hypothesis testing." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-535.
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There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model. The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation.Diss. Stockholm : Handelshögskolan, 2004
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Kibar, Mustafa Alptekin. "Building Cost Index Forecasting With Time Series Analysis." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608686/index.pdf.
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Building cost indices are widely used in construction industry to measure the rate of change of building costs as a combination of labor and material costs. Cost index forecast is crucial for the two main parties of construction industry, contactor, and the client. Forecast information is used to increase the accuracy of estimate for the project cost to evaluate the bid price.
The aim of this study is to develop time series models to forecast building cost indices in Turkey and United States. The models developed are compared with regression analysis and simple averaging models in terms of predictive accuracy. As a result of this study, time series models are selected as the most accurate models in predicting cost indices for both Turkey and United States. Future values of building cost indices can be predicted in adequate precision using time series models. This useful information can be used in tender process in estimation of project costs, which is one of the critical factors affecting the overall success of a construction project. Better cost estimates shall enable contractors to produce cash flow forecasts more acurately. Furthermore accurate prediction of future prices is very useful for owners in budget allocationsmoreover can help investors to evaluate project alternatives adequately.
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Knight, Marina Iuliana. "A second generation wavelet construction and applications to regression and time series analysis." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425147.
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Korkas, Karolos. "Randomised and L1-penalty approaches to segmentation in time series and regression models." Thesis, London School of Economics and Political Science (University of London), 2014. http://etheses.lse.ac.uk/1032/.
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It is a common approach in statistics to assume that the parameters of a stochastic model change. The simplest model involves parameters than can be exactly or approximately piecewise constant. In such a model, the aim is the posteriori detection of the number and location in time of the changes in the parameters. This thesis develops segmentation methods for non-stationary time series and regression models using randomised methods or methods that involve L1 penalties which force the coefficients in a regression model to be exactly zero. Randomised techniques are not commonly found in nonparametric statistics, whereas L1 methods draw heavily from the variable selection literature. Considering these two categories together, apart from other contributions, enables a comparison between them by pointing out strengths and weaknesses. This is achieved by organising the thesis into three main parts. First, we propose a new technique for detecting the number and locations of the change-points in the second-order structure of a time series. The core of the segmentation procedure is the Wild Binary Segmentation method (WBS) of Fryzlewicz (2014), a technique which involves a certain randomised mechanism. The advantage of WBS over the standard Binary Segmentation lies in its localisation feature, thanks to which it works in cases where the spacings between change-points are short. Our main change-point detection statistic is the wavelet periodogram which allows a rigorous estimation of the local autocovariance of a piecewise-stationary process. We provide a proof of consistency and examine the performance of the method on simulated and real data sets. Second, we study the fused lasso estimator which, in its simplest form, deals with the estimation of a piecewise constant function contaminated with Gaussian noise (Friedman et al. (2007)). We show a fast way of implementing the solution path algorithm of Tibshirani and Taylor (2011) and we make a connection between their algorithm and the taut-string method of Davies and Kovac (2001). In addition, a theoretical result and a simulation study indicate that the fused lasso estimator is suboptimal in detecting the location of a change-point. Finally, we propose a method to estimate regression models in which the coefficients vary with respect to some covariate such as time. In particular, we present a path algorithm based on Tibshirani and Taylor (2011) and the fused lasso method of Tibshirani et al. (2005). Thanks to the adaptability of the fused lasso penalty, our proposed method goes beyond the estimation of piecewise constant models to models where the underlying coefficient function can be piecewise linear, quadratic or cubic. Our simulation studies show that in most cases the method outperforms smoothing splines, a common approach in estimating this class of models.
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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.
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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore-
casting the future behavior of a multivariate time series data.
The system has two components combined by means of a system interface.
First, a rule extraction method is designed which is named Fuzzy MAR (Multivari-
ate Auto-regression). The method produces the temporal relationships between
each of the variables and past values of all variables in the multivariate time series
system in the form of fuzzy rules. These rules may constitute the rule-base in a
fuzzy expert system.
Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in -
time is designed in order to make the use of fuzzy rules, to provide an environment
that keeps temporal relationships between the variables and to forecast the future
behavior of data. The rule base of ANFIS unfolded in time contains temporal
TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation
learning algorithm is used. The system takes the multivariate data and the num-
ber of lags needed which are the output of Fuzzy MAR in order to describe a
variable and predicts the future behavior.
Computer simulations are performed by using synthetic and real multivariate
data and a benchmark problem (Gas Furnace Data) used in comparing neuro-
fuzzy systems. The tests are performed in order to show how the system efficiently
model and forecast the multivariate temporal data. Experimental results show
that the proposed model achieves online learning and prediction on temporal data.
The results are compared by other neuro-fuzzy systems, specifically ANFIS.
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Stark, J. Alex. "Statistical model selection techniques for data analysis." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390190.
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Mohr, Maria [Verfasser], and Natalie [Akademischer Betreuer] Neumeyer. "Changepoint detection in a nonparametric time series regression model / Maria Mohr ; Betreuer: Natalie Neumeyer." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2018. http://d-nb.info/1171988303/34.
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Mohr, Maria Verfasser], and Natalie [Akademischer Betreuer] [Neumeyer. "Changepoint detection in a nonparametric time series regression model / Maria Mohr ; Betreuer: Natalie Neumeyer." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2018. http://nbn-resolving.de/urn:nbn:de:gbv:18-94167.
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Ljung, Carolina, and Maria Svedberg. "A Study of Momentum Effects on the Swedish Stock Market using Time Series Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228996.
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This study investigates if momentum effects can be found on the Swedish stock market by testing a cross-sectional momentum strategy on historical data. To explain the results mathematically, a second approach, involving time series regression for predicting future returns is introduced and thereby extends the cross-sectional theory. The result of the study shows that momentum effects through the cross-sectional strategy exist on the Swedish stock market. Although positive return is found, the time series regression do not give any significance for predicting future returns. Hence, there is a contradiction between the two approaches.Denna studie undersöker om momentumeffekter föreligger på den svenska aktiemarknaden med hjälp av två olika tillvägagångssätt. Först testas momentumstrategin på historisk data och därefter genomförs tidseriesregression för att undersöka om resultaten har statistisk signifikans för att prediktera framtida avkastning. Resultatet visar att momentumeffekter existerar på den svenska aktiemarknaden. Trots att positiv avkastning erhålls ger tidserieregressionen ingen indikation på att prediktering av framtida avkastning är möjlig. Följaktligen finns det en motsägelse mellan de två tillvägagångssätten.
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Katardjiev, Nikola. "High-variance multivariate time series forecasting using machine learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353827.
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There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag.The models were trained on clinical trial data of patients in an alcohol addiction treatment plan provided by a Uppsala-based company. The results showed moderate performance differences, with a concern that the models were performing a random walk or naive forecast. Further analysis was able to prove that at least one model, the feed-forward neural network, was not undergoing this and was able to make meaningful forecasts one time step into the future. In addition, the research also examined the effec tof optimization processes by comparing a grid search, a random search, and a Bayesian optimization process. In all cases, the grid search found the lowest minima, though its slow runtimes were consistently beaten by Bayesian optimization, which contained only slightly lower performances than the grid search.Det finns flera verktyg och modeller inom maskininlärning som kan användas för att utföra tidsserieprognoser, men det är sällan tydligt vilken modell som är lämplig vid val, då olika modeller är anpassade för olika sorts data. Denna forskning har som mål att undersöka problemet genom att träna fyra modeller - support vector machine, random forest, ett neuralt nätverk, och ett LSTM-nätverk - på en flervariabelstidserie med hög varians för att förutse trendskillnader ett tidssteg framåt i tiden, kontrollerat för tidsfördröjning. Modellerna var tränade på klinisk prövningsdata från patienter som deltog i en alkoholberoendesbehandlingsplan av ett Uppsalabaserat företag. Resultatet visade vissa moderata prestandaskillnader, och en oro fanns att modellerna utförde en random walk-prognos. I analysen upptäcktes det dock att den ena neurala nätverksmodellen inte gjorde en sådan prognos, utan utförde istället meningsfulla prediktioner. Forskningen undersökte även effekten av optimiseringsprocesser genomatt jämföra en grid search, random search, och Bayesisk optimisering. I alla fall hittade grid search lägsta minimumpunkten, men dess långsamma körtider blev konsistent slagna av Bayesisk optimisering, som även presterade på nivå med grid search.
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Koons, Bruce K. "Parameter estimation for series observed with round-off error." Diss., Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/54221.
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Time series data often is observed with measurement error. One type of measurement error almost always present is rounding error. A procedure is proposed for estimating parameters of a finite moving average time series which is observed only after rounding.
Method of moments estimators are proposed for estimation of parameters of time series observed with general measurement error, including error, εn, which is correlated with the series Xt being measured. This procedure requires knowledge of the autocovariance function (ACF) of εt, and the cross covariances between Xt and εr.
For rounding error, the rounding error series is shown to approach uniform white noise as the rounding interval width, R, approaches zero, and the cross correlations between Xt, and rounding error εt, are shown to approach zero as R -> 0. For both small R and large R, the ACF of εt, and the cross covariances between Xt and εt, are approximated. These values are then used to estimate the parameters of the moving average model for Xt when Xt is observed after rounding.Ph. D.
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Premanode, Bhusana. "Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23954.
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Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.
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Yu, Mingyu Carleton University Dissertation Mathematics. "Nested-error regression models and small area estimation combining cross-sectional and time series data." Ottawa, 1993.
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Mei, Jiali. "Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS578/document.
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Nous sommes intéressé par la reconstitution et la prédiction des séries temporelles multivariées à partir des données partiellement observées et/ou agrégées.La motivation du problème vient des applications dans la gestion du réseau électrique.Nous envisageons des outils capables de résoudre le problème d'estimation de plusieurs domaines.Après investiguer le krigeage, qui est une méthode de la litérature de la statistique spatio-temporelle, et une méthode hybride basée sur le clustering des individus, nous proposons un cadre général de reconstitution et de prédiction basé sur la factorisation de matrice nonnégative.Ce cadre prend en compte de manière intrinsèque la corrélation entre les séries temporelles pour réduire drastiquement la dimension de l'espace de paramètres.Une fois que le problématique est formalisé dans ce cadre, nous proposons deux extensions par rapport à l'approche standard.La première extension prend en compte l'autocorrélation temporelle des individus.Cette information supplémentaire permet d'améliorer la précision de la reconstitution.La deuxième extension ajoute une composante de régression dans la factorisation de matrice nonnégative.Celle-ci nous permet d'utiliser dans l'estimation du modèle des variables exogènes liées avec la consommation électrique, ainsi de produire des facteurs plus interprétatbles, et aussi améliorer la reconstitution.De plus, cette méthod nous donne la possibilité d'utiliser la factorisation de matrice nonnégative pour produire des prédictions.Sur le côté théorique, nous nous intéressons à l'identifiabilité du modèle, ainsi qu'à la propriété de la convergence des algorithmes que nous proposons.La performance des méthodes proposées en reconstitution et en prédiction est testé sur plusieurs jeux de données de consommation électrique à niveaux d'agrégation différentsWe are interested in the recovery and prediction of multiple time series from partially observed and/or aggregate data.Motivated by applications in electricity network management, we investigate tools from multiple fields that are able to deal with such data issues.After examining kriging from spatio-temporal statistics and a hybrid method based on the clustering of individuals, we propose a general framework based on nonnegative matrix factorization.This frameworks takes advantage of the intrisic correlation between the multivariate time series to greatly reduce the dimension of the parameter space.Once the estimation problem is formalized in the nonnegative matrix factorization framework, two extensions are proposed to improve the standard approach.The first extension takes into account the individual temporal autocorrelation of each of the time series.This increases the precision of the time series recovery.The second extension adds a regression layer into nonnegative matrix factorization.This allows exogenous variables that are known to be linked with electricity consumption to be used in estimation, hence makes the factors obtained by the method to be more interpretable, and also increases the recovery precision.Moreover, this method makes the method applicable to prediction.We produce a theoretical analysis on the framework which concerns the identifiability of the model and the convergence of the algorithms that are proposed.The performance of proposed methods to recover and forecast time series is tested on several multivariate electricity consumption datasets at different aggregation level
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Cancado, Luciana Pacheco. "Economic growth panel data evidence from Latin America /." Ohio : Ohio University, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1127143858.
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Šuľan, Matej. "Finanční analýza společnosti s využitím systému Maple." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2019. http://www.nusl.cz/ntk/nusl-402150.
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The diploma thesis deals with the financial analysis of the selected company. On the analysis base of ratio financial indicators, time series, regression analysis and with using of the Maple system, the past and actual financial situation have been evaluated and future potential development of the company has been predicted.
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Pitrun, Ivet 1959. "A smoothing spline approach to nonlinear inference for time series." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8367.
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Shain, Cory Adam. "Language, time, and the mind: Understanding human language processing using continuous-time deconvolutional regression." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619002281033782.
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Sapankevych, Nicholas. "Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5569.
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Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting, weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms.
One type of time series interpolation and prediction algorithm that has been proven to be effective for these various types of applications is Support Vector Regression (SVR) [1], which is based on the Support Vector Machine (SVM) developed by Vapnik et al. [2, 3]. The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.
As with most time series prediction algorithms, there are typically challenges associated in applying a given heuristic to any general problem. One difficult challenge in using SVR to solve these types of problems is the selection of free parameters associated with the SVR algorithm. There is no given heuristic to select SVR free parameters and the user is left to adjust these parameters in an ad hoc manner.
The focus of this dissertation is to present an alternative to the typical ad hoc approach of tuning SVR for time series prediction problems by using Particle Swarm Optimization (PSO) to assist in the SVR free parameter selection process. Developed by Kennedy and Eberhart [4-8], PSO is a technique that emulates the process living creatures (such as birds or insects) use to discover food resources at a given geographic location. PSO has been proven to be an effective technique for many different kinds of optimization problems [9-11].
The focus of this dissertation is to present an alternative to the typical ad hoc approach of tuning SVR for time series prediction problems by using Particle Swarm Optimization (PSO) to assist in the SVR free parameter selection process. Developed by Kennedy and Eberhart [4-8], PSO is a technique that emulates the process living creatures (such as birds or insects) use to discover food resources at a given geographic location. PSO has been proven to be an effective technique for many different kinds of optimization problems [9-11].
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Koláček, Jozef. "Podpora v rozhodovacích procesech použitím analýzy časových řad." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222829.
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This master’s thesis determines qualities of the application used as a supporting tool in decision-making processes and which is useful for automatization of time series analysis. The thesis describes solution created by following the formulated criteria. It shows examples of use of the created application in praxis and interprets the outputs.
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Pukajová, Zuzana. "Posouzení vybraných ukazatelů firmy pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-224893.
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The diploma thesis focuses on assessment of given financial indicators of chosen company. It is predicted the future development of chosen financial indicators using appropriate statistical methods. It contains assessment of financial situation of analysed company and solutions of its present situation.
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Guzman, Martha Elva Ramierez. "Characterization of the association between short-term variations in daily mortality and adverse environmental conditions using time series methodology." Thesis, University of Reading, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.253129.
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Liu, Jie. "Failure prognostics by support vector regression of time series data under stationary/nonstationary environmental and operational conditions." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0019/document.
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Ce travail de thèse est motivée par la possibilité de surveiller l'état des composants de systèmes d'énergie pour leur utilisation prolongée et sécuritaire, conformément à la pratique correcte de fonctionnement et des politiques adéquates de maintenance. La motivation est de développer des méthodes basées sur la régression à vecteurs de support pour la prédiction de données de séries chronologiques dans des conditions environnementales et opérationnelles stationnaire/ non-stationnaire. Les simples modèles et les ensembles de modèles à base de SVR sont développés pour attaquer la prédiction basée sur des petits et des grands ensembles de données. Des stratégies sont proposées pour la mise à jour de façon adaptative les simples modèles et les ensembles de modèles à base de SVR au cas du changement de la distribution générant les données. Les comparaisons avec d'autres méthodes d'apprentissage en ligne sont fournies en référence à des données de séries chronologiques d'un composant critique dans les centrales nucléaires fournis par Electricité de France (EDF). Les résultats montrent que les approches proposées permettent d'atteindre des résultats de prédiction comparables compte tenu de l'erreur quadratique moyenne et erreur relative, en beaucoup moins de temps de calcul. Par ailleurs, en analysant le sens géométrique de la méthode de la sélection de vecteurs caractéristiques(FVS) proposé dans la littérature, une nouvelle méthode géométriquement interprétable, nommé Reduced RankKernel Ridge Regression-II (RRKRR-II), est proposée pour décrire les relations linéaires entre un valeur prédite et les valeurs prédites des vecteurs caractéristiques sélectionné par FVS. Les comparaisons avec plusieurs méthodes sur un certain nombre de données publics prouvent la bonne précision de la prédiction et le réglage facile des hyperparamètres de RRKRR-IIThis Ph. D. work is motivated by the possibility of monitoring the conditions of components of energy systems for their extended and safe use, under proper practice of operation and adequate policies of maintenance. The aim is to develop a Support Vector Regression (SVR)-based framework for predicting time series data under stationary/nonstationary environmental and operational conditions. Single SVR and SVR-based ensemble approaches are developed to tackle the prediction problem based on both small and large datasets. Strategies are proposed for adaptively updating the single SVR and SVR-based ensemble models in the existence of pattern drifts. Comparisons with other online learning approaches for kernel-based modelling are provided with reference to time series data from a critical component in Nuclear Power Plants (NPPs) provided by Electricité de France (EDF). The results show that the proposed approaches achieve comparable prediction results, considering the Mean Squared Error (MSE) and Mean Relative Error (MRE), in much less computation time. Furthermore, by analyzing the geometrical meaning of the Feature Vector Selection (FVS) method proposed in the literature, a novel geometrically interpretable kernel method, named Reduced Rank Kernel Ridge Regression-II (RRKRR-II), is proposed to describe the linear relations between a predicted value and the predicted values of the Feature Vectors (FVs) selected by FVS. Comparisons with several kernel methods on a number of public datasets prove the good prediction accuracy and the easy-of-tuning of the hyperparameters of RRKRR-II
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Coroneo, Laura. "Essays on modelling and forecasting financial time series." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210284.
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This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.
The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.
The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished
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Palmquist, Jacob. "How to identify downturns within an office submarke : A quantitative time series analysis of Stockholm CBD." Thesis, KTH, Fastigheter och byggande, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230936.
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The last couple of years there has been a significant increase in demand of attractive office locations in Stockholm consequently leading to all-time low office prime yields within the Central Business District (CBD), indicating warning signals regarding an overheated submarket. As the real estate market is crucial for the economy as a whole, it is essential to improve the understanding and predictability of future real estate cycles. This study produced three different logistic regression models with the purpose of identifying downturns in the office market of Stockholm CBD. The most successful model were able to predict 74 % of the actual downturns occurring throughout 114 observed quarters between Q3 1989 and Q4 2017. The dependent downturn variable consist of prime yield explained by variables on a national basis combined with submarket specific variables. Another produced model contained variables regarding confidence and expectations of tenants in Stockholm. However that model was unsatisfactory, leading to this study’s suggestion of further research on fluctuations of demand related to the current characteristics of Stockholm CBD.Under de senaste åren har det skett en betydande ökning av efterfrågan på attraktiva kontorslokaler i Stockholm vilket resulterat i rekordlåga direktavkastningskrav inom Stockholm Central Business District (CBD), vilket indikerar på varningssignaler avseende en överhettad delmarknad. Eftersom fastighetsmarknaden är avgörande för ekonomin som helhet är det viktigt att förbättra förståelsen och förutsägbarheten för framtida fastighetscykler. Denna studie producerade tre olika logistiska regressionsmodeller med syfte att identifiera nedgångar i kontorsmarknaden inom Stockholm CBD. Den mest framgångsrika modellen kunde förutse 74 % av de faktiska nedgångarna som inträffade under 114 observerade kvartal mellan Q3 1989 och Q4 2017. Den beroende nedgångsvariabeln består av prime yield som förklaras av variabler på nationell basis i kombination med delmarknadsspecifika variabler. En annan producerad modell innehöll variabler avseende förtroende och förväntningar hos hyresgäster i Stockholm. Denna modell var dock otillfredsställande, vilket ledde till att denna studie föreslog ytterligare forskning om fluktuationer i efterfrågan relaterade till de nuvarande egenskaperna hos Stockholms centralbank
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Geller, Juliane [Verfasser], Michael H. [Gutachter] Neumann, and Gustau [Gutachter] Camps-Valls. "Improved local polynomial estimation in nonparametric time series regression / Juliane Geller ; Gutachter: Michael H. Neumann, Gustau Camps-Valls." Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177603314/34.
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Zhou, Min. "The estimation and inference of complex models." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/387.
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In this thesis, we investigate the estimation problem and inference problem for the complex models. Two major categories of complex models are emphasized by us, one is generalized linear models, the other is time series models. For the generalized linear models, we consider one fundamental problem about sure screening for interaction terms in ultra-high dimensional feature space; for time series models, an important model assumption about Markov property is considered by us. The first part of this thesis illustrates the significant interaction pursuit problem for ultra-high dimensional models with two-way interaction effects. We propose a simple sure screening procedure (SSI) to detect significant interactions between the explanatory variables and the response variable in the high or ultra-high dimensional generalized linear regression models. Sure screening method is a simple, but powerful tool for the first step of feature selection or variable selection for ultra-high dimensional data. We investigate the sure screening properties of the proposal method from theoretical insight. Furthermore, we indicate that our proposed method can control the false discovery rate at a reasonable size, so the regularized variable selection methods can be easily applied to get more accurate feature selection in the following model selection procedures. Moreover, from the viewpoint of computational efficiency, we suggest a much more efficient algorithm-discretized SSI (DSSI) to realize our proposed sure screening method in practice. And we also investigate the properties of these two algorithms SSI and DSSI in simulation studies and apply them to some real data analyses for illustration. For the second part, our concern is the testing of the Markov property in time series processes. Markovian assumption plays an extremely important role in time series analysis and is also a fundamental assumption in economic and financial models. However, few existing research mainly focused on how to test the Markov properties for the time series processes. Therefore, for the Markovian assumption, we propose a new test procedure to check if the time series with beta-mixing possesses the Markov property. Our test is based on the Conditional Distance Covariance (CDCov). We investigate the theoretical properties of the proposed method. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained, and the power of the test procedure under local alternative hypothesizes have been studied. Simulation studies are conducted to demonstrate the finite sample performance of our test.
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Glore, Mary Lee. "The Threshold Prior in Bayesian Hypothesis Testing." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546.
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Switlyk, Victoria Switlyk. "Model Comparison for the Prediction of Stock Prices in the NYSE." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530869448495865.
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Novacic, Jelena, and Kablai Tokhi. "Implementation of Anomaly Detection on a Time-series Temperature Data set." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20375.
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Aldrig har det varit lika aktuellt med hållbar teknologi som idag. Behovet av bättre miljöpåverkan inom alla områden har snabbt ökat och energikonsumtionen är ett av dem. En enkel lösning för automatisk kontroll av energikonsumtionen i smarta hem är genom mjukvara. Med dagens IoT teknologi och maskinlärningsmodeller utvecklas den mjukvarubaserade hållbara livsstilen allt mer. För att kontrollera ett hushålls energikonsumption måste plötsligt avvikande beteenden detekteras och regleras för att undvika onödig konsumption. Detta examensarbete använder en tidsserie av temperaturdata för att implementera detektering av anomalier. Fyra modeller implementerades och testades; en linjär regressionsmodell, Pandas EWM funktion, en EWMA modell och en PEWMA modell. Varje modell testades genom att använda dataset från nio olika lägenheter, från samma tidsperiod. Därefter bedömdes varje modell med avseende på Precision, Recall och F-measure, men även en ytterligare bedömning gjordes för linjär regression med R^2-score. Resultaten visar att baserat på noggrannheten hos varje modell överträffade PEWMA de övriga modellerna. EWMA modeller var något bättre än den linjära regressionsmodellen, följt av Pandas egna EWM modell.Today's society has become more aware of its surroundings and the focus has shifted towards green technology. The need for better environmental impact in all areas is rapidly growing and energy consumption is one of them. A simple solution for automatically controlling the energy consumption of smart homes is through software. With today's IoT technology and machine learning models the movement towards software based ecoliving is growing. In order to control the energy consumption of a household, sudden abnormal behavior must be detected and adjusted to avoid unnecessary consumption. This thesis uses a time-series data set of temperature data for implementation of anomaly detection. Four models were implemented and tested; a Linear Regression model, Pandas EWM function, an exponentially weighted moving average (EWMA) model and finally a probabilistic exponentially weighted moving average (PEWMA) model. Each model was tested using data sets from nine different apartments, from the same time period. Then an evaluation of each model was conducted in terms of Precision, Recall and F-measure, as well as an additional evaluation for Linear Regression, using R^2 score. The results of this thesis show that in terms of accuracy, PEWMA outperformed the other models. The EWMA model was slightly better than the Linear Regression model, followed by the Pandas EWM model.
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Kumbala, Bharadwaj Reddy. "Predictive Maintenance of NOx Sensor using Deep Learning : Time series prediction with encoder-decoder LSTM." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18668.
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In automotive industry there is a growing need for predicting the failure of a component, to achieve the cost saving and customer satisfaction. As failure in a component leads to the work breakdown for the customer. This paper describes an effort in making a prediction failure monitoring model for NOx sensor in trucks. It is a component that used to measure the level of nitrogen oxide emission from the truck. The NOx sensor has chosen because its failure leads to the slowdown of engine efficiency and it is fragile and costly to replace. The data from a good and contaminated NOx sensor which is collated from the test rigs is used the input to the model. This work in this paper shows approach of complementing the Deep Learning models with Machine Learning algorithm to achieve the results. In this work LSTMs are used to detect the gain in NOx sensor and Encoder-Decoder LSTM is used to predict the variables. On top of it Multiple Linear Regression model is used to achieve the end results. The performance of the monitoring model is promising. The approach described in this paper is a general model and not specific to this component, but also can be used for other sensors too as it has a universal kind of approach.
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Stevens, James G. "An investigation of multivariate adaptive regression splines for modeling and analysis of univariate and semi-multivariate time series systems." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26601.
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Smith, Jardus. "The determinants of the international demand for tourism to South Africa / J. Smith." Thesis, North-West University, 2006. http://hdl.handle.net/10394/1275.
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Globally, the tourism industry is recognised as one of the fastest growing industries,
generating high revenues and creating a vast number of job opportunities. In South
Africa, this is no different and, in recent years, the tourism industry has outshone the
country's gold exports therefore claiming its position as the fourth highest earner of
foreign exchange to date. Yet the industry is still to receive the attention it deserves from
conventional economics. This research aimed to fill this gap in South Africa by providing
an understanding on the determinants of international tourism demand for South Africa.
The first objective of the study was to provide a broad overview of the tourism industry
of South Africa. The discussion focused on the supply and demand sides of tourism
which, in turn, are divided into the domestic and international tourism markets. There has
been a high growth, especially in the international market since 1994 and, while domestic
and international markets continue to grow, seasonality remains an issue. Tourism has a
significant impact on economic activity, employment, and the balance of payments and
therefore the industry has great potential.
The second objective was to create a theoretical understanding on the different factors
that could determine the international demand for the tourism product. From this
discussion it was found that there are various economic and non-economic factors that
are believed to have an influence on tourism demand. Income, prices, transport cost, and
the exchange rate are amongst the favourite economic variables with travel time,
population, marketing expenditure, climate, and capacity being the more popular noneconomic
factors. Among these, certain threats were also identified that could have
harmful impacts on tourism growth.
The third objective and main aim of the study was to determine which of the factors
identified earlier determine the demand for international tourism to South Africa. This
was done through an empirical investigation. Data from all the continents were used to
attain an international perspective on tourist arrivals (tourism demand). The results
indicated that capacity and climate factors determine tourism demand in the short term
with income and transport cost influencing South Africa as a tourism destination in the
long term.
The last objective was to determine whether certain events or disasters that take place
globally have a negative influence on tourism demand to South Africa. The event that
was looked as was the terror attacks on the United States in September 2001. It was
found that although the overall tourism activity of the world became stagnant during this
period, the effect was not that considerable in South Africa's tourism arrivals. Tourism in
countries such as the United Sates, on the other hand, has still not recovered fully after
this event.Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2007.
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Horta, Eduardo de Oliveira. "Essays in nonparametric econometrics and infinite dimensional mathematical statistics." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/133007.
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A presente Tese de Doutorado é composta de quatro artigos científicos em duas áreas distintas. Em Horta, Guerre e Fernandes (2015), o qual constitui o Capítulo 2 desta Tese, é proposto um estimador suavizado no contexto de modelos de regressão quantílica linear (Koenker e Basset, 1978). Uma representação de Bahadur-Kiefer uniforme é obtida, a qual apresenta uma ordem assintótica que domina aquela correspondente ao estimador clássico. Em seguida, prova-se que o viés associado à suavização é negligenciável, no sentido de que o termo de viés é equivalente, em primeira ordem, ao verdadeiro parâmetro. A taxa precisa de convergência é dada, a qual pode ser controlada uniformemente pela escolha do parâmetro de suavização. Em seguida, são estudadas propriedades de segunda ordem do estimador proposto, em termos do seu erro quadrático médio assintótico, e mostra-se que o estimador suavizado apresenta uma melhoria em relação ao usual. Como corolário, tem-se que o estimador é assintoticamente normal e consistente à ordem p n. Em seguida, é proposto um estimador consistente para a matriz de covariância assintótica, o qual não depende de estimação de parâmetros auxiliares e a partir do qual pode-se obter diretamente intervalos de confiança assintóticos. A qualidade do método proposto é por fim ilustrada em um estudo de simulação. Os artigos Horta e Ziegelmann (2015a, 2015b, 2015c) se originam de um ímpeto inicial destinado a generalizar os resultados de Bathia et al. (2010). Em Horta e Ziegelmann (2015a), Capítulo 3 da presente Tese, é investigada a questão de existência de certos processos estocásticos, ditos processos conjugados, os quais são conduzidos por um segundo processo cujo espaço de estados tem como elementos medidas de probabilidade. Através dos conceitos de coerência e compatibilidade, obtémse uma resposta afirmativa à questão anterior. Baseado nas noções de medida aleatória (Kallenberg, 1973) e desintegração (Chang e Pollard, 1997; Pollard, 2002), é proposto um método geral para construção de processos conjugados. A teoria permite um rico conjunto de exemplos, e inclui uma classe de modelos de mudança de regime. Em Horta e Ziegelmann (2015b), Capítulo 4 desta Tese, é proposto – em relação com a construção obtida em Horta e Ziegelmann (2015a) – o conceito de processo fracamente conjugado: um processo estocástico real a tempo contínuo, conduzido por uma sequência de funções de distribuição aleatórias, ambos conectados por uma condição de compatibilidade a qual impõe que aspectos da distribuição do primeiro processo são divisíveis em uma quantidade enumerável de ciclos, dentro dos quais este tem como marginais, precisamente, o segundo processo. Em seguida, mostra-se que a metodologia de Bathia et al. (2010) pode ser aplicada para se estudar a estrutura de dependência de processos fracamente conjugados, e com isso obtém-se resultados de consistência à ordem p n para os estimadores que surgem naturalmente na teoria. Adicionalmente, a metodologia é ilustrada através de uma implementação a dados financeiros. Especificamente, o método proposto permite que características da dinâmica das distribuições de processos de retornos sejam traduzidas em termos de um processo escalar latente, a partir do qual podem ser obtidas previsões de quantidades associadas a essas distribuições. Em Horta e Ziegelmann (2015c), Capítulo 5 da presente Tese, são obtidos resultados de consistência à ordem p n em relação à estimação de representações espectrais de operadores de autocovariância de séries de tempo Hilbertianas estacionárias, em um contexto de medições imperfeitas. Os resultados são uma generalização do método desenvolvido em Bathia et al. (2010), e baseiam-se no importante fato de que elementos aleatórios em um espaço de Hilbert separável são quase certamente ortogonais ao núcleo de seu respectivo operador de covariância. É dada uma prova direta deste fato.The present Thesis is composed of 4 research papers in two distinct areas. In Horta, Guerre, and Fernandes (2015), which constitutes Chapter 2 of this Thesis, we propose a smoothed estimator in the framework of the linear quantile regression model of Koenker and Bassett (1978). A uniform Bahadur-Kiefer representation is provided, with an asymptotic rate which dominates the standard quantile regression estimator. Next, we prove that the bias introduced by smoothing is negligible in the sense that the bias term is firstorder equivalent to the true parameter. A precise rate of convergence, which is controlled uniformly by choice of bandwidth, is provided. We then study second-order properties of the smoothed estimator, in terms of its asymptotic mean squared error, and show that it improves on the usual estimator when an optimal bandwidth is used. As corollaries to the above, one obtains that the proposed estimator is p n-consistent and asymptotically normal. Next, we provide a consistent estimator of the asymptotic covariance matrix which does not depend on ancillary estimation of nuisance parameters, and from which asymptotic confidence intervals are straightforwardly computable. The quality of the method is then illustrated through a simulation study. The research papers Horta and Ziegelmann (2015a;b;c) are all related in the sense that they stem from an initial impetus of generalizing the results in Bathia et al. (2010). In Horta and Ziegelmann (2015a), Chapter 3 of this Thesis, we address the question of existence of certain stochastic processes, which we call conjugate processes, driven by a second, measure-valued stochastic process. We investigate primitive conditions ensuring existence and, through the concepts of coherence and compatibility, obtain an affirmative answer to the former question. Relying on the notions of random measure (Kallenberg (1973)) and disintegration (Chang and Pollard (1997), Pollard (2002)), we provide a general approach for construction of conjugate processes. The theory allows for a rich set of examples, and includes a class of Regime Switching models. In Horta and Ziegelmann (2015b), Chapter 4 of the present Thesis, we introduce, in relation with the construction in Horta and Ziegelmann (2015a), the concept of a weakly conjugate process: a continuous time, real valued stochastic process driven by a sequence of random distribution functions, the connection between the two being given by a compatibility condition which says that distributional aspects of the former process are divisible into countably many cycles during which it has precisely the latter as marginal distributions. We then show that the methodology of Bathia et al. (2010) can be applied to study the dependence structure of weakly conjugate processes, and therewith provide p n-consistency results for the natural estimators appearing in the theory. Additionally, we illustrate the methodology through an implementation to financial data. Specifically, our method permits us to translate the dynamic character of the distribution of an asset returns process into the dynamics of a latent scalar process, which in turn allows us to generate forecasts of quantities associated to distributional aspects of the returns process. In Horta and Ziegelmann (2015c), Chapter 5 of this Thesis, we obtain p n-consistency results regarding estimation of the spectral representation of the zero-lag autocovariance operator of stationary Hilbertian time series, in a setting with imperfect measurements. This is a generalization of the method developed in Bathia et al. (2010). The generalization relies on the important property that centered random elements of strong second order in a separable Hilbert space lie almost surely in the closed linear span of the associated covariance operator. We provide a straightforward proof to this fact.
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Wang, Zilong. "Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models." UKnowledge, 2012. http://uknowledge.uky.edu/statistics_etds/3.
Full textAbstract:
Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time.
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Sando, Simon Andrew. "Estimation of a class of nonlinear time series models." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15985/1/Simon_Sando_Thesis.pdf.
Full textAbstract:
The estimation and analysis of signals that have polynomial phase and constant or time-varying amplitudes with the addititve noise is considered in this dissertation.Much work has been undertaken on this problem over the last decade or so, and there are a number of estimation schemes available. The fundamental problem when trying to estimate the parameters of these type of signals is the nonlinear characterstics of the signal, which lead to computationally difficulties when applying standard techniques such as maximum likelihood and least squares. When considering only the phase data, we also encounter the well known problem of the unobservability of the true noise phase curve.
The methods that are currently most popular involve differencing in phase followed by regression, or nonlinear transformations. Although these methods perform quite well at high signal to noise ratios, their performance worsens at low signal to noise, and there may be significant bias. One of the biggest problems to efficient estimation of these models is that the majority of methods rely on sequential estimation of the phase coefficients, in that the highest-order parameter is estimated first, its contribution removed via demodulation, and the same procedure applied to estimation of the next parameter and so on. This is clearly an issue in that errors in estimation of high order parameters affect the ability to estimate the lower order parameters correctly. As a result, stastical analysis of the parameters is also difficult.
In thie dissertation, we aim to circumvent the issues of bias and sequential estiamtion by considering the issue of full parameter iterative refinement techniques. ie. given a possibly biased initial estimate of the phase coefficients, we aim to create computationally efficient iterative refinement techniques to produce stastically efficient estimators at low signal to noise ratios. Updating will be done in a multivariable manner to remove inaccuracies and biases due to sequential procedures.
Stastical analysis and extensive simulations attest to the performance of the schemes that are presented, which include likelihood, least squares and bayesian estimation schemes. Other results of importance to the full estimatin problem, namely when there is error in the time variable, the amplitude is not constant, and when the model order is not known, are also condsidered.