Background: Response bias can distort treatment effect estimates and inferences in clinical trials. Although prevention, quantification, and adjustments have been developed, current methods are not applicable when subject-level reliability is used as the measure of response bias. Thus, the objective of the current study is to develop, test, and recommend a series of bias correction strategies for use in these cases. Methods: Monte Carlo simulation and logistic regression modeling were used to develop the strategies, examining the collective impact of sample size (N), effect size (ES), reliability distribution, and response style on estimating the treatment effect size in a series of hypothetical clinical trials. The strategies included a linear (LW), quadratic (QW), or cubic weight (CW) applied to the subject-level reliability; a reliability threshold (%); or a combination of the two (W-%). Bias and percent relative root mean square error (RRMSE (%)) were calculated for each treatment effect estimate and RRMSE (%) was compared to inform the bias correction recommendations. Results: The following recommendations are made for each N and ES combination: N=200/ES=small: no adjustment, N=200/ES=medium: 40%-LW, N=200/ES=large: 40%-QW, N=2000/ES=small: 40%-LW, N=2000/ES=medium: 55%-CW, N=2000/ES=large: 75%-CW, N=20000/ES=small: 70%-CW, N=20000/ES=medium: 85%-CW, N=20000/ES=large: 95%-CW. Conclusion: Employing these bias correction strategies in clinical trials where subject-level reliability can be calculated will decrease error and increase accuracy of estimates and validity of inferences.

A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.

No description available.

Water resource planning and management practices in the southeastern United States may be vulnerable to climate change. This vulnerability has not been quantified, and decision makers, although generally concerned, are unable to appreciate the extent of the possible impact of climate change nor formulate and adopt mitigating management strategies. Thus, this dissertation aims to fulfill this need by generating decision worthy data and information using an integrated climate change assessment framework. To begin this work, we develop a new joint variable spatial downscaling technique for statistically downscaling gridded climatic variables to generate high-resolution, gridded datasets for regional watershed modeling and assessment. The approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. In the second component of the integrated assessment framework, we develop a data-driven, downward hydrological watershed model for transforming the climate variables obtained from the downscaling procedures to hydrological variables. The watershed model includes several water balance elements with nonlinear storage-release functions. The release functions and parameters are data driven and estimated using a recursive identification methodology suitable for multiple, inter-linked modeling components. The model evolves from larger spatial/temporal scales down to smaller spatial/temporal scales with increasing model structure complexity. For ungauged or poorly-gauged watersheds, we developed and applied regionalization hydrologic models based on stepwise regressions to relate the parameters of the hydrological models to observed watershed responses at specific scales. Finally, we present the climate change assessment results for six river basins in the southeastern United States. The historical (baseline) assessment is based on climatic data for the period 1901 through 2009. The future assessment consists of running the assessment models under all IPCC A1B and A2 climate scenarios for the period from 2000 through 2099. The climate assessment includes temperature, precipitation, and potential evapotranspiration; the hydrology assessment includes primary hydrologic variables (i.e., soil moisture, evapotranspiration, and runoff) for each watershed.

In recent years, there has been a surge of research interest in the analysis of time series and spatial data. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. This dissertation addresses the development of statistical inference procedures for data exhibiting dependencies of varied form and structure. In the first work, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study. The next work considers nonparametric inference on spatial data. In this work the asymptotic distribution of the Discrete Fourier Transformation (DFT) of spatial data under pure and mixed increasing domain spatial asymptotic structures are studied under both deterministic and stochastic spatial sampling designs. The deterministic design is specified by a scaled version of the integer lattice in IRd while the data-sites under the stochastic spatial design are generated by a sequence of independent random vectors, with a possibly nonuniform density. A detailed account of the asymptotic joint distribution of the DFTs of the spatial data is given which, among other things, highlights the effects of the geometry of the sampling region and the spatial sampling density on the limit distribution. Further, it is shown that in both deterministic and stochastic design cases, for "asymptotically distant" frequencies, the DFTs are asymptotically independent, but this property may be destroyed if the frequencies are "asymptotically close". Some important implications of the main results are also given.

L’étude de la variabilité du climat est désormais indispensable pour anticiper les conséquences des changements climatiques futurs. Nous disposons pour cela de quantité de données issues de modèles de circulation générale (GCMs). Néanmoins, ces modèles ne permettent qu’une résolution partielle des interactions entre le climat et les activités humaines entre autres parce que ces modèles ont des résolutions spatiales souvent trop faibles. Il existe aujourd’hui toute une variété de modèles répondant à cette problématique et dont l’objectif est de générer des variables climatiques à l’échelle locale àpartir de variables à grande échelle : ce sont les modèles de régionalisation ou encore appelés modèles de réduction d’échelle spatiale ou de downscaling en anglais.Cette thèse a pour objectif d’approfondir les connaissances à propos des modèles de downscaling statistiques (SDMs) parmi lesquels on retrouve plusieurs approches. Le travail s’articule autour de quatre objectifs : (i) comparer des modèles de réduction d’échelle statistiques (et dynamiques), (ii) étudier l’influence des biais des GCMs sur les SDMs au moyen d’une procédure de correction de biais, (iii) développer un modèle de réduction d’échelle qui prenne en compte la non-stationnarité spatiale et temporelle du climat dans un contexte de modélisation dite spatiale et enfin, (iv) établir une définitiondes saisons à partir d’une modélisation des régimes de circulation atmosphérique ou régimes de temps.L’intercomparaison de modèles de downscaling a permis de mettre au point une méthode de sélection de modèles en fonction des besoins de l’utilisateur. L’étude des biais des GCMs révèle une influence indéniable de ces derniers sur les sorties de SDMs et les apports de la correction des biais. Les différentes étapes du développement d’un modèle spatial de réduction d’échelle donnent des résultats très encourageants. La définition des saisons par des régimes de temps se révèle être un outil efficace d’analyse et de modélisation saisonnière.Tous ces travaux de “Climatologie Statistique” ouvrent des perspectives pertinentes, non seulement en termes méthodologiques ou de compréhension de climat à l’échelle locale, mais aussi d’utilisations par les acteurs de la société. / The study of climate variability is vital in order to understand and anticipate the consequences of future climate changes. Large data sets generated by general circulation models (GCMs) are currently available and enable us to conduct studies in that direction. However, these models resolve only partially the interactions between climate and human activities, namely du to their coarse resolution. Nowadays there is a large variety of models coping with this issue and aiming at generating climate variables at local scale from large-scale variables : the downscaling models.The aim of this thesis is to increase the knowledge about statistical downscaling models (SDMs) wherein there is many approaches. The work conducted here pursues four main goals : (i) to discriminate statistical (and dynamical) downscaling models, (ii) to study the influences of GCMs biases on the SDMs through a bias correction scheme, (iii) to develop a statistical downscaling model accounting for climate spatial and temporal non-stationarity in a spatial modelling context and finally, (iv) to define seasons thanks to a weather typing modelling.The intercomparison of downscaling models led to set up a model selection methodology according to the end-users needs. The study of the biases of the GCMs reveals the impacts of those biases on the SDMs simulations and the positive contributions of the bias correction procedure. The different steps of the spatial SDM development bring some interesting and encouraging results. The seasons defined by the weather regimes are relevant for seasonal analyses and modelling.All those works conducted in a “Statistical Climatologie” framework lead to many relevant perspectives, not only in terms of methodology or knowlegde about local-scale climate, but also in terms of use by the society.

<p>  </p> <p>The management of water resources in East Africa is inherently challenged by rainfall variability and the uneven spatial distribution of freshwater resources. In addition to these issues, meteorological and water data collection has been inconsistent over the past decades, and unclearly defined purposes or end goals for collected data have left many datasets ineffectively curated. In light of the data intensiveness of current modelling and planning methods, data scarcity and inaccessibility have become substantial impediments to informed decision-making. Among the outputs of this research are 1) a revised technique for evaluating bias correction performance on reanalysis data for use in regions where precipitation data is temporally discontinuous which can potentially be applied to other types of climate data as well, 2) a new methodology for quantifying qualitative information contained in legislation and official documents and websites for the assessment of relationships between documented meteorological and water data policies and resulting outcomes in terms of data availability and accessibility, and 3) a fresh look at data needs and the value data holds with respect to water resources decision-making and management in the region.</p>

[pt] O Brasil sempre foi um país que teve sua matriz elétrica pautada majoritariamente em fontes renováveis, mais especificamente na hídrica. Com passar dos anos, esta tem se diversificado e demonstrado uma maior participação da fonte eólica. Para melhor explorála, pesquisas visando modelar seu comportamento são essenciais. Entretanto, não é sempre que se tem dados de velocidade do vento e de geração eólica disponíveis em quantidade e nas localidades de interesse. Esses dados são primordiais para identificar potenciais locais de instalação de parques eólicos, melhorar o desempenho dos existentes e estimular pesquisas de previsão e simulação da geração eólica que são entradas para auxiliar na melhor performance do planejamento e da operação do setor elétrico brasileiro. Na carência de dados de velocidade do vento, uma alternativa é o uso de dados vindos de base de reanálises. Elas disponibilizam longos históricos de dados de variáveis climáticas e atmosféricas para diversos pontos do globo terrestre e de forma gratuita. Desta forma, a primeira contribuição deste trabalho teve como foco a verificação da representatividade dos dados de velocidade do vento, disponibilizados pelo MERRA-2, no território brasileiro. Seguindo as recomendações da literatura, utilizou-se técnicas de interpolação, extrapolação e correção de viés para melhorar a adequação as velocidades fornecidas pela base de reanalise as que acontecem na altura dos rotores das turbinas dos parques eólicos. Em uma segunda contribuição combinou-se os dados do MERRA-2 com os de potência medidas em parques eólicos brasileiros para modelar de modo estocástico e não paramétrico a relação existente entre a velocidade e potência nas turbinas eólicas. Para isto utilizou-se as técnicas de clusterização, estimação das curvas de densidade e simulação. Por fim, em uma terceira contribuição, desenvolveu-se um aplicativo, no ambiente shiny, para disponibilizar as metodologias desenvolvidas nas duas primeiras contribuições. / [en] Brazil s energy landscape has historically relied heavily on renewable sources, notably hydropower, with wind energy emerging as a significant contributor in recent years. Understanding and harnessing the potential of wind energy necessitates robust modeling of its behavior. However, obtaining comprehensive wind speed and generation data, particularly in specific locations of interest, remains a challenge. In the absence of wind speed data, an alternative is to use data from a reanalysis database. They provide long histories of data on climatic and atmospheric variables for different parts of the world, free of charge. Therefore, the first contribution of this work focused on verifying the representativeness of wind speed data made available by MERRA-2 in Brazilian territory. Following literature recommendations, interpolation, extrapolation, and bias correction techniques were used to improve the adequacy of the speeds provided by the reanalysis based on those that occur at the height of the wind farm turbine rotors. In a second contribution, MERRA-2 data was combined with power measured in Brazilian wind farms to model in a stochastic and non-parametric way the relationship between speed and power in wind turbines. For this purpose, clustering, density curve estimation, and simulation techniques were used. Finally, the research culminates in the development of an application within the Shiny environment, offering a user-friendly platform to access and apply the methodologies devised in the preceding analyses. By making these methodologies readily accessible, the application facilitates broader engagement and utilization within the research community and industry practitioners alike.

Wind power presently plays a minor role in Western Canada as compared to hydroelectric power in British Columbia and coal and natural gas thermal power generation in Alberta. However, ongoing reductions in the cost of wind power generation facilities and the increasing costs of conventional power generation, particularly if the cost to the environment is included, suggest that assessment of the present and future wind field in Western Canada is of some importance. To assess present wind power, raw hourly wind speeds and homogenized monthly mean wind speeds from 30 stations in Western Canada were analyzed over the period 1971-2000 (past). The hourly data were adjusted using the homogenized monthly means to attempt to compensate for differences in anemometer height from the standard height of 10m and changes in observing equipment at stations. A regional reanalysis product, the North American Regional Reanalysis (NARR), and simulations conducted with the Canadian Regional Climate Model (CRCM) driven with global reanalysis boundary forcing, were compared to the adjusted station wind-speed time-series and probability distributions. The NARR had a better temporal correlation with the observations, than the CRCM. We posit this is due to the NARR assimilating regional observations, whereas the CRCM did not. The NARR was generally worse than the CRCM in reproducing the observed speed distribution, possibly due to the crude representation of the regional topography in NARR. While the CRCM was run at both standard (45 km) and fine (15 km) resolution, the fine grid spacing does not always provide better results: the character of the surrounding topography appears to be an important factor for determining the level of agreement. Multiple simulations of the CRCM at the 45 km resolution were also driven by two global climate models (GCMs) over the periods 1971-2000 (using only historic emissions) and 2031-2060 (using the A2 emissions scenario). In light of the CRCM biases relative to the observations, these simulations were calibrated using quantile-quantile matching to the adjusted station observations to obtain ensembles of 9 and 25 projected wind speed distributions for the 2031-2060 period (future) at the station locations. Both bias correction and change factor techniques were used for calibration. At most station locations modest increases in mean wind speed were found for most of the projected distributions, but with a large variance. Estimates of wind power density for the projected speed distributions were made using a relationship between wind speed and power from a CRCM simulation for both time periods using the 15km grid. As would be expected from the wind speed results and the proportionality of wind power to the cube of wind speed, wind power at the station locations is more likely than not to increase in the 2031-2060 period from the 1971-2000 period. Relative changes in mean wind speeds at station locations were found to be insensitive to the station observations and choice of calibration technique, suggesting that we estimate relative change at all 45km grid points using all pairs of past/future mean wind speeds from the CRCM simulations. Overall, our results suggest that wind energy resources in Western Canada are reasonably likely to increase at least modestly in the future. / Graduate / 0725 / 0608 / jtdaines@uvic.ca

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-25T14:46:06Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE VERSÃO FINAL (CD).pdf: 688894 bytes, checksum: fc5c0291423dc50d4989c1c2d8d4af65 (MD5) / Made available in DSpace on 2017-04-25T14:46:06Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE VERSÃO FINAL (CD).pdf: 688894 bytes, checksum: fc5c0291423dc50d4989c1c2d8d4af65 (MD5) Previous issue date: 2016-11-04 / Young e Bakir (1987) propôs a classe de Modelos Lineares Log-Gama Generalizados (MLLGG) para analisar dados de sobrevivência. No nosso trabalho, estendemos a classe de modelos propostapor Young e Bakir (1987) permitindo uma estrutura não linear para os parâmetros de regressão. A nova classe de modelos é denominada como Modelos Não Lineares Log-Gama Generalizados (MNLLGG). Com o objetivo de obter a correção de viés de segunda ordem dos estimadores de máxima verossimilhança (EMV) na classe dos MNLLGG, desenvolvemos uma expressão matricial fechada para o estimador de viés de Cox e Snell (1968). Analisamos, via simulação de Monte Carlo, os desempenhos dos EMV e suas versões corrigidas via Cox e Snell (1968) e através da metodologia bootstrap (Efron, 1979). Propomos também resíduos e técnicas de diagnóstico para os MNLLGG, tais como: alavancagem generalizada, influência local e influência global. Obtivemos, em forma matricial, uma expressão para o fator de correção de Bartlett à estatística da razão de verossimilhanças nesta classe de modelos e desenvolvemos estudos de simulação para avaliar e comparar numericamente o desempenho dos testes da razão de verossimilhanças e suas versões corrigidas em relação ao tamanho e poder em amostras finitas. Além disso, derivamos expressões matriciais para os fatores de correção tipo-Bartlett às estatísticas escore e gradiente. Estudos de simulação foram feitos para avaliar o desempenho dos testes escore, gradiente e suas versões corrigidas no que tange ao tamanho e poder em amostras finitas. / Young e Bakir (1987) proposed the class of generalized log-gamma linear regression models (GLGLM) to analyze survival data. In our work, we extended the class of models proposed by Young e Bakir (1987) considering a nonlinear structure for the regression parameters. The new class of models is called generalized log-gamma nonlinear regression models (GLGNLM). We also propose matrix formula for the second-order bias of the maximum likelihood estimate of the regression parameter vector in the GLGNLM class. We use the results by Cox and Snell (1968) and bootstrap technique [Efron (1979)] to obtain the bias-corrected maximum likelihood estimate. Residuals and diagnostic techniques were proposed for the GLGNLM, such as generalized leverage, local and global influence. An general matrix notation was obtained for the Bartlett correction factor to the likelihood ratio statistic in this class of models. Simulation studies were developed to evaluate and compare numerically the performance of likelihood ratio tests and their corrected versions regarding size and power in finite samples. Furthermore, general matrix expressions were obtained for the Bartlett-type correction factor for the score and gradient statistics. Simulation studies were conducted to evaluate the performance of the score and gradient tests with their corrected versions regarding to the size and power in finite samples.