Dissertations / Theses: 'Spatial analysis (Statistics) Regression analysis'

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Yue, Yu. "Spatially adaptive priors for regression and spatial modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6059.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.

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Sikdar, Khokan Chandra. "Application of geographically weighted regression for assessing spatial non-stationarity /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,172881.

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Wheeler, David C. "Diagnostic tools and remedial methods for collinearity in linear regression models with spatially varying coefficients." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155413322.

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Burke, Tommy. "Evaluation of visualisations of geographically weighted regression, with perceptual stability." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/15680.

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Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.

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Kordi, Maryam. "Geographically weighted spatial interaction (GWSI)." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4112.

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One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.

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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.

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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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Sha, Zhe. "Estimation of conditional auto-regressive models." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6cc56943-2b4d-4931-895a-f3ab67e48e3a.

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Conditional auto-regressive (CAR) models are frequently used with spatial data. However, the likelihood of such a model is expensive to compute even for a moderately sized data set of around 1000 sites. For models involving latent variables, the likelihood is not usually available in closed form. In this thesis we use a Monte Carlo approximation to the likelihood (extending the approach of Geyer and Thompson (1992)), and develop two strategies for maximising this. One strategy is to limit the step size by defining an experimental region using a Monte Carlo approximation to the variance of the estimates. The other is to use response surface methodology. The iterative procedures are fully automatic, with user-specified options to control the simulation and convergence criteria. Both strategies are implemented in our R package mclcar. We demonstrate aspects of the algorithms on simulated data on a torus, and achieve similar results to others in a short computational time on two datasets from the literature. We then use the methods on a challenging problem concerning forest restoration with data from around 7000 trees arranged in transects within study plots. We modelled the growth rate of the trees by a linear mixed effects model with CAR spatial error and CAR random e ects for study plots in an acceptable computational time. Our proposed methods can be used for similar models to provide a clearly defined framework for maximising Monte Carlo approximations to likelihoods and reconstructing likelihood surfaces near the maximum.

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Martinho, Maria. "Spatial analysis of exposure coefficients with applications to stomach cancer." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:427fe13e-39b1-4bfd-a3a8-be957120cf44.

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Earlier ecological studies on the relation between H. pylori infection and stomach cancer have considered that the relation between these two variables, as estimated by the exposure coefficient, is constant. However, there is evidence to suggest that this relation changes geographically due to differences in strains of H. pylori. Since the prevalence of H. pylori varies with socio-economic status, the association between the latter and stomach cancer mortality may also vary geographically. This thesis studies stomach cancer by taking into account the geographical variability of the exposure coefficients. The study proposes the use of regression mixtures, clustering models and spatially varying regressions for the study of varying exposure coefficients. The effect of transformations of variables in these models appears to have been little considered. We provide new necessary conditions for invariance under transformations of variables for mixed effect models in general, and for the proposed models in particular. In addition, we show that varying exposure coefficients may induce a varying baseline risk. The regression mixtures and the clustering model are applied to a data set on stomach cancer incidence and H. pylori prevalence in 57 countries worldwide. We extend the clustering model to reflect any distance measure between the geographical units, including the Euclidean distance, in the formation of clusters. We also show that the clustering model performs better than the regression mixture model when the aim is to identify connected clusters and the observations present large variance. The results obtained with the clustering model supported the existence of three clusters where the interaction between the human and H. pylori populations have similar characteristics. Spatially varying regressions are applied to a data set of areal death counts of stomach cancer and spending power in 275 counties in continental Portugal. We provide an original strategy for implementing multivectorial intrinsic autoregressions as the distribution for the random effects. The results obtained with the application of this methodology were consistent with a varying exposure coefficient of spending power.

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Huang, Fang. "Modeling patterns of small scale spatial variation in soil." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-011106-155345/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: spatial variations; nested random effects models; semivariogram models; kriging methods; multiple logistic regression models; missing; multiple imputation. Includes bibliographical references (p. 35-36).

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Chun, Yongwan. "Behavioral specifications of network autocorrelation in migration modeling an analysis of migration flows by spatial filtering /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187188476.

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Blazzard, Kimberly. "Geostatistical Analysis of Potential Sinkhole Risk: Examining Spatial and Temporal Climate Relationships in Tennessee and Florida." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3426.

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Sinkholes are a significant hazard for the southeastern United States. Although differences in climate are known to affect karst environments differently, quantitative analyses correlating sinkhole formation with climate variables is lacking. A temporal linear regression for Florida sinkholes and two modeled regressions for Tennessee sinkholes were produced: a general linearized logistic regression and a MaxEnt derived species distribution model. Temporal results showed highly significant correlations with precipitation, teleconnection patterns, temperature, and CO2, while spatial results showed highly significant correlations with precipitation, wind speed, solar radiation, and maximum temperature. Regression results indicated that some sinkhole formation variability could be explained by these climatological patterns and could possibly be used to help predict when/where sinkholes may form in the future.

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Sener, Ipek Nese. "An Innovative Model Integrating Spatial And Statistical Analyses For A Comprehensive Traffic Accident Study." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606148/index.pdf.

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The negative social and economic results of traffic accidents are the most serious problems within the concept of traffic safety. Every year, unfortunately, a huge number of traffic accidents result in destructive losses. Especially, when the holiness of human life is concerned, traffic safety has an invaluable role for the traffic improvement strategies. In this manner, Turkey places one of the highest ranks regarding the growing rate and severity of traffic accidents that should be immediately taken under control. In this study, an innovative model that constructs a hybrid between the spatial and statistical analyses is developed in order to examine the importance of enhancing statistical analysis with georeferenced data and so location-based studies in traffic accident analysis. Meanwhile, the effects of road characteristic and environment are considered for exploring the integral role of roadway factor to the occurrence of accidents, and consequently for emphasizing easily applicable and controllable engineering safety measures. Because of the rare and random distribution of traffic accident data, logistic regression is used for the statistical part of the study in order to find the pairwise risk factors among the roadway and environmental parameters. After unifying these relative risk factors with the logic of Analytic Hierarchy Process, the finalized accident risk factors are attached to the digitized road characteristics map through Geographic Information Systems (GIS). The abilities of GIS in mapping, displaying and overlaying different data sets ensure to visualize high risked accident areas with their corresponding potential causal factors. The integration of statistical and spatial analyses is essential for developing appropriate and effective precautions in addition to its easily understandable, applicable and modifiable structure. Finally, the model is proven to be appropriate for both interpreting the existing traffic accident problem or potential future accidents and also developing comprehensive and reliable location-based safety studies.

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Costa, Julio C. "Analysis and optimization of empirical path loss models and shadowing effects for the Tampa Bay area in the 2.6 GHz band." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002547.

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Goldman, Gretchen Tanner. "Characterization and impact of ambient air pollution measurement error in time-series epidemiologic studies." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41158.

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Time-series studies of ambient air pollution and acute health outcomes utilize measurements from fixed outdoor monitoring sites to assess changes in pollution concentration relative to time-variable health outcome measures. These studies rely on measured concentrations as a surrogate for population exposure. The degree to which monitoring site measurements accurately represent true ambient concentrations is of interest from both an etiologic and regulatory perspective, since associations observed in time-series studies are used to inform health-based ambient air quality standards. Air pollutant measurement errors associated with instrument precision and lack of spatial correlation between monitors have been shown to attenuate associations observed in health studies. Characterization and adjustment for air pollution measurement error can improve effect estimates in time-series studies. Measurement error was characterized for 12 ambient air pollutants in Atlanta. Simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. This method allows for pollutant-specific quantification of impacts of measurement error on health effect estimates, both the assessed strength of association and its significance. To inform on the amount and type of error present in Atlanta measurements, air pollutant concentrations were simulated over the 20-county metropolitan area for a 6-year period, incorporating several distribution characteristics observed in measurement data. The simulated concentration fields were then used to characterize the amount and type of error due to spatial variability in ambient concentrations, as well as the impact of use of different exposure metrics in a time-series epidemiologic study. Finally, methodologies developed for the Atlanta area were applied to air pollution measurements in Dallas, Texas with consideration for the impact of this error on a health study of the Dallas-Fort Worth region that is currently underway.

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Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.

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Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.

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Hu, ChungLynn. "Nonignorable nonresponse in the logistic regression analysis /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.

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Butler, Thomas W. "Spatial statistics and analysis of earth's ionosphere." Thesis, Boston University, 2013. https://hdl.handle.net/2144/10950.

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Thesis (Ph.D.)--Boston University
The ionosphere, a layer of Earths upper atmosphere characterized by energetic charged particles, serves as a natural plasma laboratory and supplies proxy diagnostics of space weather drivers in the magnetosphere and the solar wind. The ionosphere is a highly dynamic medium, and the spatial structure of observed features (such as auroral light emissions, charge density, temperature, etc.) is rich with information when analyzed in the context of fluid, electromagnetic, and chemical models. Obtaining measurements with higher spatial and temporal resolution is clearly advantageous. For instance, measurements obtained with a new electronically-steerable incoherent scatter radar (ISR) present a unique space-time perspective compared to those of a dish-based ISR. However, there are unique ambiguities for this modality which must be carefully considered. The ISR target is stochastic, and the fidelity of fitted parameters (ionospheric densities and temperatures) requires integrated sampling, creating a tradeoff between measurement uncertainty and spatio-temporal resolution. Spatial statistics formalizes the relationship between spatially dispersed observations and the underlying process(es) they represent. A spatial process is regarded as a random field with its distribution structured (e.g., through a correlation function) such that data, sampled over a spatial domain, support inference or prediction of the process. Quantification of uncertainty, an important component of scientific data analysis, is a core value of spatial statistics. This research applies the formalism of spatial statistics to the analysis of Earth's ionosphere using remote sensing diagnostics. In the first part, we consider the problem of volumetric imaging using phased-array ISR based on optimal spatial prediction ("kriging"). In the second part, we develop a technique for reconstructing two-dimensional ion flow fields from line-of-sight projections using Tikhonov regularization. In the third part, we adapt our spatial statistical approach to global ionospheric imaging using total electron content (TEC) measurements derived from navigation satellite signals.

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Liu, Hai Chan Kung-sik. "Semiparametric regression analysis of zero-inflated data." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/308.

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Maimon, Geva. "A Bayesian spatial analysis of glass data /." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82284.

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In criminal investigations involving glass evidence, refractive index (RI) is the property of glass most commonly used by forensic examiners to determine the association between control samples of glass obtained at the crime scene, and samples of glass found on a suspect. Previous studies have shown that an intrinsic variability of RI exists within a pane of float glass. In this thesis, we attempt to determine whether this variability is spatially determined or random in nature, the conclusion of which plays an important role in the statistical interpretation of glass evidence. We take a Bayesian approach in fitting a spatial model to our data, and utilize the WinBUGS software to perform Gibbs sampling. To test for spatial variability, we propose two test quantities, and employ Bayesian Monte Carlo significance tests to test our data, as well as nine other specifically formulated data-sets.

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Van, Deventer Petrus Jacobus Uys. "Outliers, influential observations and robust estimation in non-linear regression analysis and discriminant analysis." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/4363.

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Wilson, Helen Elizabeth. "Statistical analysis of replicated spatial point patterns." Thesis, Lancaster University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268009.

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The field of pathology provides us with many opportunities for collecting replicated spatial data. Using an ordinary microscope, for example, we can digitise cell positions within windows imposed on pieces of tissue. Suppose now that we have some such replicated spatial data from several groups of individuals, where each point in each window represents a cell position. We seek to determine whether the spatial arrangement of cells differs between the groups. We propose and develop a new method which allows us to answer such questions, and apply it to some spatial neuro-anatomical data. We introduce point process theory, and extend the existing second order methods to deal with replicated spatial data. We conclude the first part of the thesis by defining Sudden Infant Death Syndrome (S.LD.S.) and Intra-Uterine Growth Retardation (LU.G.R.), and stating why these conditions are neuro-anato,mically interesting. We develop and validate a method for comparing groups of spatial data, which is motivated by analysis of variance, and uses a Monte Carlo procedure to attach significance to between-group differences. Having carried out our initial investigative work looking exclusively at the one-way set up, we extend the new methods to cope with two and higher way set ups, and again carry out some validation. We turn our attention to practical issues which arise in the collection of spatial neuroanatomical data. How, for example, should we collect the data to ensure the unbiasedness of any inference we may draw from it? We introduce the field of stereology which facilitates the unbiased sampling of tissue. We note a recent proposal to assess spatial distribution of cells using a stereological approach, and compare it with an existing second order method. We also note the level of structural heterogeneity within the brain, and consider the best way to design a sampling protocol. We conclude with a spatial analysis of cell position data, collected using our specified design, from normal birth-weight non S.LD.S., normal birth-weight S.I.D.S and low birth-weight S.LD.S cases.

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Dai, Elin, and Lara Güleryüz. "Factors that influence condominium pricing in Stockholm: A regression analysis : A regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254235.

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This thesis aims to examine which factors that are of significance when forecasting the selling price of condominiums in Stockholm city. Through the use of multiple linear regression, response variable transformation, and a multitude of methods for refining the model fit, a conclusive, out of sample validated model with a confidence level of 95% was obtained. To conduct the statistical methods, the software R was used. This study is limited to the districts of inner city Stockholm with the postal codes 112-118, and the final model can only be applied to this area as the postal codes are included as regressors in the model. The time period in which the selling price was analyzed varied between January 2014 and April 2019, in which the volatility of the time value of money has not been taken into account for the time period. The final model included the following variables as the ones having an impact on the selling price: floor, living area, monthly fee, construction year, district of the city.
Denna studie ämnar till att undersöka vilka faktorer som är av betydelse när syftet är att förutsäga prissättningen på bostadsrätter i Stockholms innerstad. Genom att använda multipel linjär regression, transformation av responsvariabeln, samt en mängd olika metoder för att förfina modellen, togs en slutgiltig, out of sample-validerad modell med ett 95%-konfidensintervall fram. För att genomföra de statistiska metoderna användes programmet R. Denna studie är avgränsad till de distrikt i Stockholms innerstad vars postnummer varierar mellan 112-118, därav är det viktigt att modellen endast appliceras på dessa områden eftersom de är inkluderade i modellen som regressorer. Tidsperioden inom vilket slutpriserna analyserades var mellan januari 2014 och april 2019, i vilket valutans volatilitet inte har analyserats som en ekonomisk påverkande faktor. Den slutgiltiga modellen innefattar de följande variablerna: våning, boarea, månadsavgift, konstruktionsår, distrikt.

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Kim, Hyon-Jung. "Nonparametric Spatial analysis in spectral and space domains." NCSU, 2000. http://www.lib.ncsu.edu/theses/available/etd-20000822-235839.

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KIM, HYON-JUNG. Variance Estimation in Spatial Regression Using a NonparametricSemivariogram Based on Residuals. (Under the direction of Professor Dennis D. Boos.)The empirical semivariogram of residuals from a regression model withstationary errors may be used to estimate the covariance structure of the underlyingprocess.For prediction (Kriging) the bias of the semivariogram estimate induced byusing residuals instead of errors has only a minor effect because thebias is small for small lags. However, for estimating the variance of estimatedregression coefficients and of predictions,the bias due to using residuals can be quite substantial. Thus wepropose a method for reducing the bias in empirical semivariogram estimatesbased on residuals. The adjusted empirical semivariogram is then isotonizedand made positive definite and used to estimate the variance of estimatedregression coefficients in a general estimating equations setup.Simulation results for least squares and robust regression show that theproposed method works well in linear models withstationary correlated errors. Spectral Analysis with Spatial Periodogram and Data Tapers.(Under the direction of Professor Montserrat Fuentes.)The spatial periodogram is a nonparametric estimate of the spectral density, which is the Fourier Transform of the covariance function. The periodogram is a useful tool to explain the dependence structure of aspatial process.Tapering (data filtering) is an effective technique to remove the edge effects even inhigh dimensional problemsand can be applied to the spatial data in order to reduce the bias of the periodogram.However, the variance of the periodogram increases as the bias is reduced.We present a method to choose an appropriate smoothing parameter for datatapers and obtain better estimates of the spectral densityby improving the properties of the periodogram.The smoothing parameter is selected taking intoaccount the trade-off between bias and variance of the taperedperiodogram. We introduce a new asymptotic approach for spatial datacalled `shrinking asymptotics', which combines theincreasing-domain and the fixed-domain asymptotics.With this approach, the tapered spatial periodogram can be usedto determine uniquely the spectral density of the stationary process,avoiding the aliasing problem.

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Zhang, Zhigang. "Nonproportional hazards regression models for survival analysis /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144473.

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Meless, Dejen. "Test Cycle Optimization using Regression Analysis." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54809.

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Industrial robots make up an important part in today’s industry and are assigned to a range of different tasks. Needless to say, businesses need to rely on their machine park to function as planned, avoiding stops in production due to machine failures. This is where fault detection methods play a very important part. In this thesis a specific fault detection method based on signal analysis will be considered. When testing a robot for fault(s), a specific test cycle (trajectory) is executed in order to be able to compare test data from different test occasions. Furthermore, different test cycles yield different measurements to analyse, which may affect the performance of the analysis. The question posed is: Can we find an optimal test cycle so that the fault is best revealed in the test data? The goal of this thesis is to, using regression analysis, investigate how the presently executed test cycle in a specific diagnosis method relates to the faults that are monitored (in this case a so called friction fault) and decide if a different one should be recommended. The data also includes representations of two disturbances.

The results from the regression show that the variation in the test quantities utilised in the diagnosis method are not explained by neither the friction fault or the test cycle. It showed that the disturbances had too large effect on the test quantities. This made it impossible to recommend a different (optimal) test cycle based on the analysis.

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Othuon, Lucas Onyango A. "The accuracy of parameter estimates and coverage probability of population values in regression models upon different treatments of systematically missing data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ34604.pdf.

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Ma, Kunlei. "Spatial Analysis of Chinese Air Transportation." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1446546987.

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Fiery, Michael Allen. "A form of two-phase sampling utilizing regression analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4312.

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Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains iv, 81 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 32).

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Zhou, Qi Jessie. "Inferential methods for extreme value regression models /." *McMaster only, 2002.

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Sandrock, Brian Arthur. "Spatial Analysis of Foreclosures in Hillsborough County." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5438.

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This study examines the spatial impact various socio-demographic and housing factors might have in the foreclosure lis pendens rate within various Hillsborough County, Florida tracts as well as comparing those results with past research. Hopefully the techniques used in this study can be implemented elsewhere in order to better study the foreclosure crisis. The methods used within this research were chosen carefully in order to best understand what is being observed. One method is OLS regression which helps see the impact of each variable and if that impact has a negative or positive effect on the rate of foreclosure. Bivariate Maps were created to spatially examine each variable when compared to the foreclosure rate as well as Effect plots from regression in order to see how the true relationship of a variable affects the foreclosure rate.

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Liu, Hai. "Semiparametric regression analysis of zero-inflated data." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/308.

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Zero-inflated data abound in ecological studies as well as in other scientific and quantitative fields. Nonparametric regression with zero-inflated response may be studied via the zero-inflated generalized additive model (ZIGAM). ZIGAM assumes that the conditional distribution of the response variable belongs to the zero-inflated 1-parameter exponential family which is a probabilistic mixture of the zero atom and the 1-parameter exponential family, where the zero atom accounts for an excess of zeroes in the data. We propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzing zero-inflated data, with the further assumption that the probability of non-zero-inflation is some monotone function of the (non-zero-inflated) exponential family distribution mean. When the latter assumption obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We develop an iterative algorithm for model estimation based on the penalized likelihood approach, and derive formulas for constructing confidence intervals of the maximum penalized likelihood estimator. Some asymptotic properties including the consistency of the regression function estimator and the limiting distribution of the parametric estimator are derived. We also propose a Bayesian model selection criterion for choosing between the unconstrained and the constrained ZIGAMs. We consider several useful extensions of the COZIGAM, including imposing additive-component-specific proportional and partial constraints, and incorporating threshold effects to account for regime shift phenomena. The new methods are illustrated with both simulated data and real applications. An R package COZIGAM has been developed for model fitting and model selection with zero-inflated data.

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Donkor, Faustina Fosua. "Spatial Analysis of Teen Births in North Central Texas." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc3056/.

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The United States has the highest teen birth rate among western industrialized countries and the highest levels of pregnancy among adolescents (Alan Guttmacher Institute, 1994). While the rate of teen births is high throughout the country, considerable variations exist between and within regions. Texas is one of the 5 leading states with the highest teen birth rates to mothers less than 18 years of age. This research provides a detailed analysis of births to mothers aged between 10 and 19 years in North Central Texas counties. Due to the modifiable area unit problem and to provide a finer geographical scale of analysis, teen births in Dallas County zip codes were examined as a special case study. Statistical and Geographic Information System (GIS) analysis reveal that race/ethnicity, education and income are significant factors in teen births in the region. Single parent households and receipt of public assistance were not statistically significant. Suggestions for reducing vulnerability to teen births are presented.

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Holmgren, Rachelle. "Challenges Involved in the Automation of Regression Analysis." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1405.

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Extracting meaningful insights from massive datasets to help guide business decisions requires specialized skills in data analysis. Unfortunately, the supply of these skills does not meet the demand, due to the massive amount of data generated by society each day. This leaves businesses with a large amount of unanalyzed data that could have been used to support business decision making. Automating the process of analyzing this data would help address many companies' key challenge of a lack of appropriate analytical skills. This paper examines the process and challenges in automating this analysis of data. Central challenges include removing outliers without context, transforming data to a format that is compatible with the analysis method that will be used, and analyzing the results of the model.

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Mitchell, Napoleon. "Outliers and Regression Models." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc279029/.

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The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.

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Luna, Ronaldo. "Liquefaction evaluation using a spatial analysis system." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/19413.

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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Master of Science
Department of Statistics
Juan Du
Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.

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Slack, Marc G. "Spatial and temporal path planning." Thesis, This resource online, 1987. http://scholar.lib.vt.edu/theses/available/etd-04272010-020255/.

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Detwiler, Dana. "Microcomputer implementation of robust regression techniques." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03302010-020305/.

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Li, Youjun. "Bayesian Non-Linear Quantile Regression with Application in Decline Curve Analysis for Petroleum Reservoirs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491607854874719.

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Jensen, Daniel. "Spatial analysis and visualization in the NBA using GIS applications." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527009.

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Basketball is a unique sport in which the use of space and time is greatly important for a team’s success. Furthermore, the National Basketball Association (NBA) is undergoing drastic change in terms of the way teams approach spatial issues as well as the spatio-temporal technologies and analytics. Given these facts, Geographic Information Systems (GIS) provide the opportunity to develop new analytic and visual methodologies to perform spatial analysis for team performances and meet the league’s changing needs. This project thus develops new approaches, methods, and toolsets using GIS to demonstrate its efficacy and potential for professional application in the NBA. The first application uses GIS to adapt Relative Motion analysis techniques to an existing play, seeking to represent the average motion characteristics entailed therein. The other application uses a tool developed to map, glean spatial statistics, and model the use and importance of floor spacing for teams in the NBA.

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Yin, Jiangyong. "Bayesian Analysis of Non-Gaussian Stochastic Processes for Temporal and Spatial Data." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406928537.

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White, Gentry. "Bayesian semiparametric spatial and joint spatio-temporal modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4450.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.

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Pereira, Sandra M. C. "Analysis of spatial point patterns using hierarchical clustering algorithms." University of Western Australia. School of Mathematics and Statistics, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0056.

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[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.

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Li, Hongfei. "Approximate profile likelihood estimation for spatial-dependence parameters." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1191267954.

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Dai, Wenlin. "Different-based methods in nonparametric regression models." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/40.

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This thesis develops some new di.erence-based methods for nonparametric regression models. The .rst part of this thesis focuses on the variance estimation for nonparametric models with various settings. In Chapter 2, a uni.ed framework of variance estimator is proposed for a model with smooth mean function. This framework combines the higher order di.erence sequence with least squares method and greatly extends the literature, including most of existing methods as special cases. We derive the asymptotic mean squared errors and make both theoretical and numerical comparison for various estimators within the system. Based on the dramatic interaction of ordinary di.erence sequences and least squares method, we eventually .nd a uniformly satisfactory estimator for all the settings, solving the challenging problem of sequence selection. In Chapter 3, three methods are developed for the variance estimation in the repeated measurement setting. Both their asymptotic properties and .nite sample performance are explored. The sequencing method is shown to be the most adaptive while the sample variance method and the partitioning method are shown to outperform in certain cases. In Chapter 4, we propose a pairwise regression method for estimating the residual variance. Speci.cally, we regress the squared di.erence between observations on the squared distance between design points, and then estimate the residual variance as the intercept. Unlike most existing di.erence-based estimators that require a smooth regression function, our method applies to regression models with jump discontinuities. And it also applies to the situations where the design points are unequally spaced. The smoothness assumption of the nonparametric regression function is quite critical for the curve .tting and the residual variance estimation. The second part (Chapter 5) concentrates on the discontinuities detection for the mean function. In particular, we revisit the di.erence-based method in M¨uller and Stadtm¨uller (1999) and propose to improve it. To achieve the goal, we .rst reveal that their method is less e.cient due to the inappropriate choice of the response variable in their linear regression model. We then propose a new regression model for estimating the residual variance and the total amount of discontinuities simultaneously. In both theory and simulations, we show that the proposed variance estimator has a smaller MSE compared to their estimator, whereas the e.ciency of the estimators for the total amount of discontinuities remain unchanged. Finally, we construct a new test procedure for detection using the newly proposed estimations; and via simulation studies, we demonstrate that our new test procedure outperforms the existing one in most settings. At the beginning of Chapter 6, a series of new di.erence sequences is de.ned to complete the span between the optimal sequence and the ordinary sequence. The variance estimators using proposed sequences are shown to be quite robust and achieve smallest mean square errors for most of general settings. Then, the di.erence-based methods for variance function estimation are generally discussed. Keywords: Asymptotic normality, Di.erence-based estimator, Di.erence sequence, Jump point, Least square, Nonparametric regression, Pairwise regression, Repeated measurement, Residual variance

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RUTHERFORD, BRIAN MILNE. "BOOTSTRAP AND RELATED METHODS FOR APPROXIMATE CONFIDENCE BOUNDS IN NONPARAMETRIC REGRESSION." Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/183923.

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The problem considered relates to estimating an arbitrary regression function m(x) from sample pairs (Xᵢ,Yᵢ) 1 ≤ i ≤ n. A model is assumed of the form Y = m(x) + ε(x) where ε(x) is a random variable with expectation 0. One well known method for estimating m(x) is by using one of a class of kernel regression estimators say m(n)(x). Schuster (1972) has shown conditions under which the limiting distribution of the kernel estimator m(n)(x) is the normal distribution. It might also be of interest to use the data to estimate the distribution of m(n)(x). One could, given this estimate, construct approximate confidence bounds for the function m(x). Three estimators are proposed for the density of m(n)(x). They share a basis in non-parametric kernel regression and utilize bootstrap techniques to obtain the density estimate. The order of convergence of one of the estimators is examined and conditions are given under which the order is higher then when estimation is by the normal approximation. Finally the performance of each estimator for constructing confidence bounds is compared for moderate sample sizes using computer studies.

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Guo, Xu. "Checking the adequacy of regression models with complex data structure." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/90.

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In this thesis, we investigate the model checking problem for parametric regression model with missing response at random and nonignorable missing response. Besides, we also propose a hypothesis-adaptive procedure which is based on the dimension reduction theory. Finally, to extend our methods to missing response situation, we consider the dimension reduction problem with missing response at random. The .rst part of the thesis introduces the model checking for parametric models with response missing at random which is a more general missing mechanism than missing completely at random. Di.erent from existing approaches, two tests have normal distributions as the limiting null distributions no matter whether the inverse probability weight is estimated parametrically or nonparametrically. Thus, p-values can be easily determined. This observation shows that slow convergence rate of nonparametric estimation does not have signi.cant e.ect on the asymptotic behaviours of the tests although it may have impact in .nite sample scenarios. The tests can detect the alternatives distinct from the null hypothesis at a nonparametric rate which is an optimal rate for locally smoothing-based methods in this area. Simulation study is carried out to examine the performance of the tests. The tests are also applied to analyze a data set on monozygotic twins for illustration. In the second part of the thesis, we consider model checking for general linear regression model with non-ignorable missing response. Based on an exponential tilting model, we .rst propose three estimators for the unknown parameter in the general linear regression model. Three empirical process-based tests are constructed. We discuss the asymptotic properties of the proposed tests under null and local alternative hypothesis with di.erent scenarios. We .nd that these three tests perform the same in the asymptotic sense. Simulation studies are also carried out to assess the performance of our proposed test procedures. In the third part, we revisit traditional local smoothing model checking procedures. Noticing that the general nonparametric regression model can be considered as a special multi-index model, we propose an adaptive testing procedure based on the dimension reduction theory. To our surprise, our method can detect local alternative at faster rate than the traditional optimal rate. The theory indicates that in model checking problem, dimensionality may not have strong impact. Simulations are carried out to examine the performance of our methodology. A real data analysis is conducted for illustration. In the last part, we study the dimension reduction problem with missing response at random. Based on the work in this part, we can extend the adaptive testing procedure introduced in the third part to the missing response situation. When there are many predictors, how to e.ciently impute responses missing at random is an important problem to deal with for regression analysis because this missing mechanism, unlike missing completely at random, is highly related to high-dimensional predictor vector. In su.cient dimension reduction framework, the fusion-re.nement (FR) method in the literature is a promising approach. To make estimation more accurate and e.cient, two methods are suggested in this paper. Among them, one method uses the observed data to help on missing data generation, and the other one is an ad hoc approach that mainly reduces the dimension in the nonparametric smoothing in data generation. A data-adaptive synthesization of these two methods is also developed. Simulations are conducted to examine their performance and a HIV clinical trial dataset is analysed for illustration. Keywords: Model checking; Inverse probability weight; Non-ignorable missing response; Adaptive; Central subspace; Dimension reduction; Data-adaptive Synthesization; Missing recovery; Missing response at random; Multiple imputation.

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Yiu, Man-lung. "Advanced query processing on spatial networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36279365.

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Yiu, Man-lung, and 姚文龍. "Advanced query processing on spatial networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36279365.

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Horiguchi, Akira. "Bayesian Additive Regression Trees: Sensitivity Analysis and Multiobjective Optimization." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1606841319315633.

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Dissertations / Theses on the topic 'Spatial analysis (Statistics) Regression analysis'

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