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This empirical study is aimed to analyze the determinants of residential water demand and performed water use practice at household level in Hawassa. This study will fill the research gap and information on factors affecting household water demand in regions being water scarce and will provide useful information for policy-makers and water utility planners in order to use scarce drinking water resource more efficiently. In this study the proposed potential factors determine household water demand in Hawassa were; Socioeconomic and demographic characteristics, the average monthly household expenditure, use of water appliances and household water use patterns for various purposes, and household awareness towards water source conservation. The cross sectional survey was done in 169 rondomly selected households. The collected Data was analyzed using multiple regression models with different functional forms (linear, semi-log) and heteroskedaticity corrected model was also used in each of functional forms to examine the structural relationship between the quantity of water demand and explanatory variables. The gretl statisitcal software package was used. The descriptive statistics analysis was also followed to present results in tables, charts and graphs (mean, median, minimum, maximum, frequency...

Function words are known to be shorter than content words. I investigate the function words BE and HAVE (with its content word homonym) and show that more reduction, operationalized as word shortening or contraction, is found in some grammaticalized meanings of these words. The difference between the words’ uses cannot be attributed to differences in frequency or semantic weight. Instead I argue that these words are often shortened and reduced when they occur in constructions in which they are highly predictable. This suggests that particular grammaticalized uses of a word are stored with their own exemplar clouds of context-specific phonetic realizations. The phonetics of any instance of a word are then jointly determined by the exemplar cloud for that word and the particular context. A given instance of an auxiliary can be reduced either because it is predictable in the current context or because that use of the auxiliary usually occurs in predictable contexts. The effects cannot be attributed to frequency or semantic weight. The present study compares function word production in the speech of school-aged children and their caregivers and in inter-adult speech. The effects of predictability in context and average predictability across contexts are replicated across the datasets. However, I find that as children get older their function words shorten relative to content words, even when controlling for increasing speech rate, showing that as their language experience increases they spend less time where it is not needed for comprehensibility. Caregivers spend less time on function words with older children than younger children, suggesting that they expect function words to be more difficult for younger interlocutors to decode than for older interlocutors. Additionally, while adults use either word shortening or contraction to increase the efficiency of speech, children tend to either use contraction and word shortening or neither until age seven, where they start to use one strategy or the other like adults. Young children with better vocabulary employ an adult-like strategy earlier, suggesting earlier onset of efficient yet effective speech behavior, namely allocating less signal to function words when they are especially easy for the listener to decode.

Master of Science / Department of Statistics / Weixing Song / The relationship between a random variable and a random vector is often investigated through the regression modeling. Because of its relative simplicity and ease of interpretation, a particular parametric form is often assumed for the regression function. If the pre-specified function form truly reflects the truth, then the resulting estimators and inference procedures would be reliable and efficient. But if the regression function does not represent the true relationship between the response and the predictors, then the inference results might be very misleading. Therefore, lack-of-fit test should be an indispensable part in regression modeling. This report compares the finite sample performance of several classical lack-of-fit tests in regression models via simulation studies. It has three chapters. The conception of the lack-of-fit test, together with its basic setup, is briefly introduced in Chapter 1; then several classical lack-of-fit test procedures are discussed in Chapter 2; finally, thorough simulation studies are conducted in Chapter 3 to assess the finite sample performance of each procedure introduced in Chapter 2. Some conclusions are also summarized in Chapter 3. A list of MATLAB codes that are used for the simulation studies is given in the appendix.

Maximum likelihood approach for independent but not identically distributed observations is studied. In the first part of the thesis, conditions for consistency and asymptotic normality of the maximum likelihood estimates for this case are stated. Uniform integrability has a major role in proving the desired properties. K-sample problem serves as an example for using the described method. The second part is focused on estimates obtained by minimizing convex functions. Convexity is a key for showing the consistency and asymptotic normality of the estimates in this case. The results can be used for maximum likelihood when observations with logconcave densities are involved. Finally, normal linear model, logistic regression and Poisson regression examples are provided to present the application of the method.

The rapid expansion of high-throughput phenotyping (HTP) platforms in agronomic research has led to a major shift in plant science towards time-series phenotyping that can track plant development through its life stages, providing an opportunity to dissect the genetic basis of longitudinal traits. Plant breeders can now phenotype large populations during the growing season and promote the desirable genetic gain for the traits of interest in specific time points within their breeding program. The biggest challenge is to use the various tools in a practical way to understand the many complexities of plant growth and development and breeding implications. This dissertation explores interdisciplinary frameworks to assess different applications of HTP for longitudinal traits in soybean breeding. We provide a review outlining the current analytical approaches in quantitative genetics and genomics to adequately use high-dimensional phenomic data. Examples, advantages, and pitfalls of each approach, and future research directions and opportunities are explored. Among longitudinal traits in soybean, average canopy coverage (ACC) and above-ground biomass (AGB) are promising traits to strategic improve yield gain. Soybean ACC is highly heritable, with a high genetic correlation to yield and can be effectively measured by unmanned aerial systems (UAS). This study reveals that progeny rows selection using yield given ACC (Yield|ACC) selected the most top-ranking lines in advanced yield trials, which emphasizes the value of HTP of ACC for selection in the early stages of soybean breeding. In addition, we developed a HTP methodology to predict soybean AGB over several days after planting (DAP) and assessed the quantitative genomic properties of temporal AGB using random regression models (RRM). Our results show that AGB narrow-sense heritability estimates fluctuated over time and the genetic correlation of AGB between DAP decreased as the days went further apart. Considering the trait heritability, high prediction accuracies suggest that AGB is a good indicator trait for genomic selection in soybean breeding. Different genomic regions were found to be associated with AGB over time with potential time-specific SNPs playing a role in the trait expression. Similarly, candidate genes were identified with potential different patterns of expression over time. This study presents novel genetic knowledge for longitudinal traits in soybean and may contribute to the development of new cultivars with high yield and optimized AGB. This is the first application of RRM for genomic evaluation of a longitudinal trait in soybean and provides a foundation that RRM can be an effective approach to understand the temporal genetic architecture of a longitudinal trait in other crops.

Energy consumption by buildings, due to various factors such as temperature regulation, lighting, poses a threat to our environment and energy resources. In the United States, statistics reveal that commercial and residential buildings combined contribute about 40 percent of the overall energy consumption, and this figure is expected to increase. In order to manage the growing demand for energy, there is a need for energy system optimization, which would require a realistic, high-resolution energy-demand model. In this work, we investigate and model the energy consumption of buildings by taking into account physical, structural, economic, and social factors that influence energy use. We propose a novel activity based modeling framework that generates an energy demand profile on a regular basis for a given nominal day.  We use this information to generate a building-level energy demand profile at highly dis-aggregated level. We then investigate the different possible uses of generated demand profiles in different What-if scenarios like urban-area planning, demand-side management, demand sensitive pricing, etc. We also provide a novel way to resolve correlational and consistency problems in the generation of individual-level and building-level "shared" activities which occur due to individuals\' interactions. / Master of Science

Master of Science / Department of Statistics / Weixin Yao / In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.

Résumé: La mesure de l’équivalent en eau de la neige (EEN) sur le terrain permet de prédire la quantité d’eau libérée par la fonte de la neige. La télédétection dans les micro-ondes passives offre le potentiel d’estimer I’EEN et peut complémenter ces observations de façon synoptique pour l’ensemble du territoire. Un produit de cartographie de I’EEN couvrant l’ensemble du globe a été élaboré par le NSIDC basé sur le capteur AMSR-E. Cet instrument, lancé en 2002, a une résolution améliorée par rapport aux capteurs antérieurs. L’estimation de I’EEN se base sur la différence entre un canal peu affecté (19 GHz) et un canal affecté (37 GHz) par la diffusion de volume de la neige. La précision de ce produit a été évaluée pour la province de Québec à l’hiver 2003 et à l’hiver 2004 qui ont un EEN moyen de 170 mm. Des sous-estimations importantes ont été révélées et une certaine difficulté à détecter la présence de neige. Des modèles régionaux de régressions linéaires ont été développés pour le Québec. Des corrections pour la fraction d’eau et de forêt ont été appliquées à la combinaison T19v.37v et ont permis d’améliorer les résultats. Ces corrections sont basées sur la température de l’air du modèle GEM. Les meilleurs résultats sont pour la classe de neige taïga à l’hiver 2003 avec une erreur relative de 24 % tandis que l’erreur relative est d’environ 40 % pour la région maritime. Les erreurs élevées dans la classe taïga ont été attribuées à des couverts de neige plus épais que la capacité de pénétration des micro-ondes tandis que les erreurs de la classe maritime a des fractions forêt élevées et à la neige mouillée. La présence d’importante quantité de neige et la forêt dense de la province de Québec compliquent l’estimation de I’EEN au Québec avec un modèle de régression. || Abstract: Snow water equivalent (SWE) measurements in the field allow estimation of the quantity of released water from the melting of snow. This is useful to predict the water reserve available for production of hydro-electricity. Remote sensing with microwave can estimate SWE and complement those observations synoptically for whole territories. A SWE mapping products was developed by NSIDC based on the AMSR-E sensor launched in 2002 with an improved resolution compared to previous sensors. SWE estimation is based on difference between a channel weakly affected (19 GHz) and a channel strongly affected by volume scattering. The precision of this product was evaluated for the province of Quebec in winter 2003 and winter 2004 with a mean SWE of 170 mm. Important underestimation and some difficulty of detecting the snow was revealed. Regional linear regression models were developed for the province of Quebec. Corrections for forest and water fraction were applied on T19V-37V combination and permit to improve the results. Those corrections were based on air temperature from the GEM model. Best results were found for taiga snow class in winter 2003 with a relative error of 28% and approximately 40% for maritime snow class. High errors in the taiga region were attributed to snow depth higher than the penetration depth of the microwave and errors in the maritime region to high forest density and wet snow. The important snow amount and high density forest of the province of Quebec hampers the estimation of SWE with a regression model.

This project implemented novel approaches to assess the source, age, concentration and mobility of fecal indicator microorganisms within a karst groundwater system. Research was conducted in the well-characterized Blue Hole Spring karst groundwater basin in Versailles, Woodford County, Kentucky. At this site the AC/TC ratio and fecal coliform (FC) bacteria counts were used to delineate sources of fecal inputs and determine relative age of the fecal matter. An aging experiment using indicator bacteria (total coliform (TC) and atypical colonies (AC)), which approximated subsurface conditions, indicated that changes in the AC/TC ratio are likely to be retarded during bacterial transport through karst conduits. Decreases in the AC/TC ratio during the monitoring period appear to be the result of sewage releases. Multiple logistic regression (MLR) modeling was performed to examine correlations between physiochemical parameters and FC concentrations. MLR models using physiochemical parameters correctly predicted “safe for contact” (< 200 cfu/100 mL FC) conditions 65.6% of the time and “unsafe for contact” (> 200 cfu/100 mL FC) conditions 69.2% of the time at Blue Hole Spring. Modeling using other indicators (TC and AC) predicted “safe for contact” conditions 87.5% of the time and “unsafe for contact” conditions 61.5% of the time. A series of tracer tests were performed to compare transport of solute and abiotic particle tracers (rhodamine WT fluorescent dye, bromide and fluorescent bacteria-sized microspheres) and bacteria (15N-enriched wild-type E. coli) within the karst system. The surrogate tracers did not suitably mimic microbial mobility within the basin. Solutes and 15N-enriched E. coli arrived concurrently during storm flow to Blue Hole Spring, whereas microsphere breakthrough corresponded with maximum solute concentrations. The 15Nenriched E. coli exhibited slightly more tailing during storm-flow recession than solute tracers, none of which exhibited remobilization. Microspheres demonstrated remobilization within the conduits that correlated with later increases in discharge related to secondary storm events.