Global ETD Search

1	Minimax D-optimal designs for regression models with heteroscedastic errors Yzenbrandt, Kai 20 April 2021 (has links) Minimax D-optimal designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error variance and use a minimax approach to define robust designs. As usual it is hard to find robust designs analytically, since the associated design problem is not a convex optimization problem. However, the minimax D-optimal design problem has an objective function as a difference of two convex functions. An effective algorithm is developed to compute minimax D-optimal designs under the least squares estimator and generalized least squares estimator. The algorithm can be applied to construct minimax D-optimal designs for any linear or nonlinear regression model with heteroscedastic errors. In addition, several theoretical results are obtained for the minimax D-optimal designs. / Graduate robust regression design minimax design D-optimal design non-convex optimization generalized least squares estimator
2	Modeling financial volatility : A functional approach with applications to Swedish limit order book data Elezovic, Suad January 2009 (has links) <!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:SV;} @page Section1 {size:612.0pt 792.0pt; margin:72.0pt 90.0pt 72.0pt 90.0pt; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> This thesis is designed to offer an approach to modeling volatility in the Swedish limit order market. Realized quadratic variation is used as an estimator of the integrated variance, which is a measure of the variability of a stochastic process in continuous time. Moreover, a functional time series model for the realized quadratic variation is introduced. A two-step estimation procedure for such a model is then proposed. Some properties of the proposed two-step estimator are discussed and illustrated through an application to high-frequency financial data and simulated experiments. In Paper I, the concept of realized quadratic variation, obtained from the bid and ask curves, is presented. In particular, an application to the Swedish limit order book data is performed using signature plots to determine an optimal sampling frequency for the computations. The paper is the first study that introduces realized quadratic variation in a functional context. Paper II introduces functional time series models and apply them to the modeling of volatility in the Swedish limit order book. More precisely, a functional approach to the estimation of volatility dynamics of the spreads (differences between the bid and ask prices) is presented through a case study. For that purpose, a two-step procedure for the estimation of functional linear models is adapted to the estimation of a functional dynamic time series model. Paper III studies a two-step estimation procedure for the functional models introduced in Paper II. For that purpose, data is simulated using the Heston stochastic volatility model, thereby obtaining time series of realized quadratic variations as functions of relative quantities of shares. In the first step, a dynamic time series model is fitted to each time series. This results in a set of inefficient raw estimates of the coefficient functions. In the second step, the raw estimates are smoothed. The second step improves on the first step since it yields both smooth and more efficient estimates. In this simulation, the smooth estimates are shown to perform better in terms of mean squared error. Paper IV introduces an alternative to the two-step estimation procedure mentioned above. This is achieved by taking into account the correlation structure of the error terms obtained in the first step. The proposed estimator is based on seemingly unrelated regression representation. Then, a multivariate generalized least squares estimator is used in a first step and its smooth version in a second step. Some of the asymptotic properties of the resulting two-step procedure are discussed. The new procedure is illustrated with functional high-frequency financial data. Financial data functional time series multivariate generalized least squares seemingly unrelated autoregression Statistics, computer and systems science Statistik, data- och systemvetenskap
3	[en] INSTITUTIONS AND MONETARY POLICY: A CROSS-COUNTRY EMPIRICAL ANALYSIS / [pt] INSTITUIÇÕES E POLÍTICA MONETÁRIA: UMA ANÁLISE EMPÍRICA DE UM CROSS-SECTION DE PAÍSES GUSTAVO AMORAS SOUZA LIMA 06 March 2018 (has links) [pt] Esse trabalho busca verificar se há relação entre a política monetária conduzida por um grupo de países e as suas instituições, especialmente aquelas ligadas ao setor público. A partir da estimação de uma regra de politica monetária comum para um grupo de países, regredimos coeficientes de reação das autoridades monetárias a desvios da inflação da meta e do hiato da atividade em métricas de instituições. Encontramos relações significativas entre a condução de política monetária e as instituições dos países, bem como potenciais determinantes das instituições, em vários casos. / [en] This paper seeks to verify if there is a relationship between the monetary policy conducted by a group of countries and their institutions, especially those related to the public sector. From the estimation of a common monetary policy rule for a group of countries, we regressed the reaction coefficients of the monetary authorities to deviations from inflation target and activity gap on institutional metrics. We find significant relationships between conducting monetary policy and country institutions, as well as potential determinants of institutions, in several cases. [pt] POLITICA MONETARIA [en] MONETARY POLICY [pt] INSTITUICAO [en] INSTITUTION [en] FEASIBLE GENERALIZED LEAST SQUARES
4	Modelo fatorial com cargas funcionais para séries temporais / Factor model with functional loadings for time series Salazar, Duvan Humberto Cataño 12 March 2018 (has links) No contexto dos modelos fatoriais existem diferentes metodologias para abordar a modelagem de séries temporais multivariadas que exibem uma estrutura não estacionária de segunda ordem, co- movimentos e transições no tempo. Modelos com mudanças estruturais abruptas e restrições rigorosas (muitas vezes irreais) nas cargas fatoriais, quando elas são funções determinísticas no tempo, foram propostos na literatura para lidar com séries multivariadas que possuem essas características. Neste trabalho, apresentamos um modelo fatorial com cargas variando continuamente no tempo para modelar séries temporais não estacionárias e um procedimento para sua estimação que consiste em dois estágios. No primeiro, os fatores latentes são estimados empregando os componentes principais das séries observadas. Em um segundo estágio, tratamos estes componentes principais como co-variáveis e as cargas funcionais são estimadas através de funções de ondaletas e mínimos quadrados generalizados. Propriedades assintóticas dos estimadores de componentes principais e de mínimos quadrados dos coeficientes de ondaletas são apresentados. O desempenho da metodologia é ilustrado através de estudos de simulação. Uma aplicação do modelo proposto no mercado spot de energia do Nord Pool é apresentado. / In the context of the factor models there are different methodologies to modeling multivariate time series that exhibit a second order non-stationary structure, co-movements and transitions over time. Models with abrupt structural changes and strict restrictions (often unrealistic) in factor loadings, when they are deterministic functions of time, have been proposed in the literature to deal with multivariate series that have these characteristics. In this work, we present a factor model with time-varying loadings continuously to modeling non-stationary time series and a procedure for its estimation that consists of two stages. First, latent factors are estimated using the principal components of the observed series. Second, we treat principal components obtained in first stage as covariate and the functional loadings are estimated by wavelet functions and generalized least squares. Asymptotic properties of the principal components estimators and least squares estimators of the wavelet coefficients are presented. The per- formance of the methodology is illustrated by simulations. An application to the model proposed in the energy spot market of the Nord Pool is presented. Approximate factor models Componentes principais Estacionaridade local Generalized least squares Local stationarity Mínimos quadrados generalizados Modelos fatoriais aproximados Ondaletas Principal components Wavelets
5	Modelo fatorial com cargas funcionais para séries temporais / Factor model with functional loadings for time series Duvan Humberto Cataño Salazar 12 March 2018 (has links) No contexto dos modelos fatoriais existem diferentes metodologias para abordar a modelagem de séries temporais multivariadas que exibem uma estrutura não estacionária de segunda ordem, co- movimentos e transições no tempo. Modelos com mudanças estruturais abruptas e restrições rigorosas (muitas vezes irreais) nas cargas fatoriais, quando elas são funções determinísticas no tempo, foram propostos na literatura para lidar com séries multivariadas que possuem essas características. Neste trabalho, apresentamos um modelo fatorial com cargas variando continuamente no tempo para modelar séries temporais não estacionárias e um procedimento para sua estimação que consiste em dois estágios. No primeiro, os fatores latentes são estimados empregando os componentes principais das séries observadas. Em um segundo estágio, tratamos estes componentes principais como co-variáveis e as cargas funcionais são estimadas através de funções de ondaletas e mínimos quadrados generalizados. Propriedades assintóticas dos estimadores de componentes principais e de mínimos quadrados dos coeficientes de ondaletas são apresentados. O desempenho da metodologia é ilustrado através de estudos de simulação. Uma aplicação do modelo proposto no mercado spot de energia do Nord Pool é apresentado. / In the context of the factor models there are different methodologies to modeling multivariate time series that exhibit a second order non-stationary structure, co-movements and transitions over time. Models with abrupt structural changes and strict restrictions (often unrealistic) in factor loadings, when they are deterministic functions of time, have been proposed in the literature to deal with multivariate series that have these characteristics. In this work, we present a factor model with time-varying loadings continuously to modeling non-stationary time series and a procedure for its estimation that consists of two stages. First, latent factors are estimated using the principal components of the observed series. Second, we treat principal components obtained in first stage as covariate and the functional loadings are estimated by wavelet functions and generalized least squares. Asymptotic properties of the principal components estimators and least squares estimators of the wavelet coefficients are presented. The per- formance of the methodology is illustrated by simulations. An application to the model proposed in the energy spot market of the Nord Pool is presented. Componentes principais Estacionaridade local Mínimos quadrados generalizados Modelos fatoriais aproximados Ondaletas Approximate factor models Generalized least squares Local stationarity Principal components Wavelets
6	The Evolution of Life History Traits and Their Thermal Plasticity in Daphnia Bowman, Larry L., Jr., Post, David M. 06 January 2023 (has links) (PDF) Few studies have explored the relative strength of ecogeographic versus lineage-specific effects on a global scale, particularly for poikilotherms, those organisms whose internal temperature varies with their environment. Here, we compile a global dataset of life history traits in Daphnia, at the species-and population-level, and use those data to parse the relative influences of lineage-specific effects and climate. We also compare the thermal response (plasticity) of life history traits and their dependence on climate, temperature, precipitation, and latitude. We found that the mode of evolution for life history traits varies but that the thermal response of life history traits most often follows a random walk model of evolution. We conclude that life history trait evolution in Daphnia is not strongly species-specific but is ecogeographically distinct, suggesting that life history evolution should be understood at the population level for Daphnia and possibly for other poikilotherms. zooplankton phylogenetic generalized least squares phylogenetic independent contrasts Daphnia phylogeny temperature climate metaanalysis Curriculum and Instruction Environmental Sciences Marine Biology
7	Statistical modelling of return on capital employed of individual units Burombo, Emmanuel Chamunorwa 10 1900 (has links) Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics) Classical multiple linear regression Principal components regression Generalized least squares regression Robust maximum likelihood regression Return on capital employed Stepwise directed search Kaiser-Guttman criterion Key determinants 519.536 Regression analysis Principal components analysis Multivariate analysis Linear models (statistics) Mathematical statistics
8	Statistical modelling of return on capital employed of individual units Burombo, Emmanuel Chamunorwa 10 1900 (has links) Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics) Classical multiple linear regression Principal components regression Generalized least squares regression Robust maximum likelihood regression Return on capital employed Stepwise directed search Kaiser-Guttman criterion Key determinants 519.536 Regression analysis Principal components analysis Multivariate analysis Linear models (statistics) Mathematical statistics
9	具有額外或不足變異的群集類別資料之研究 / A Study of Modelling Categorical Data with Overdispersion or Underdispersion 蘇聖珠, Su, Sheng-Chu Unknown Date (has links) 進行調查時，最後的抽樣單位常是從不同的群集取得的，而同一群集內的樣本對象，因背景類似而對於某些問題常會傾向相同或類似的反應，研究者若忽略這種群內相關性，仍以獨立性樣本進行分析時，因其共變異數矩陣通常會與多項模式的共變異數矩陣相差懸殊，而造成所謂的額外變異或不足變異的現象。本文在不同的情況下，提出了Dirichlet-Multinomial模式(簡稱DM模式)、擴展的DM模式、以及兩種平均數-共變異數矩陣模式，以適當的彙整所有的群集資料。並討論DM與EDM模式中相關之參數及格機率之最大概似估計法，且分別對此兩種平均數-共變異數矩陣模式，提出求導廣義最小平方估計的程序。此外，也針對幾種特殊的二維表及三維表結構，探討對應的參數及格機率之估計方法。並提出計算簡易的Score統計檢定量以判斷群內相關(intra-cluster correlation)之存在性，及判斷資料集具有額外或不足變異，而對於不同母體的群內相關同質性檢定亦提出討論。 / This paper presents a modelling method of analyzing categorical data with overdispersion or underdispersion.　In many studies, data are collected from differ clusters, and members within the same cluster behave similary.　Thus, the responses of members within the same cluster are not independent and the multinomial distribution is not the correct distribution for the observed counts.　Therefore, the covariance matrix of the sample proportion vector tends to be much different from that of the multinomial model.　We discuss four different models to fit counts data with overdispersion or underdispersion feature, witch include Dirichlet-Multinomial model (DM model), extended DM model (EDM model), and two mean-covariance models.　Method of maximum-likelihood estimation is discussed for DM and EDM models.　Procedures to derive generalized least squares estimates are proposed for the two mean-covariance models respectively. As to the cell probabilities, we also discuss how to estimate them under several special structures of　two-way and three-way tables.　More easily evaluated Score test statistics are derived for the DM and EDM models to test the existence of　the intra-cluster correlation.　And the test of homogeneity of intra-cluster correlation among several populations is also derived. 群集類別資料群內相關性額外變異不足變異 Dirichlet-Multinomial模式最大概似估計式廣義最小平方估計式 categorical data intra-cluster correlation overdispersion underdispersion Dirichlet-Multinomial model maximum-likelihood estimation generalized least squares estimation
10	Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield Maas, Bea 20 November 2013 (has links) No description available. 630 birds bats arthropods multitrophic interactions agroforestry tropical landscape forest margin landscape functional diversity crop yield exclosure experiments experimental exclusion arthropod suppression avian predation ecosystem services mist netting point counts artificial plasticine caterpillar dummy experiment biological control monitoring mapping Lemon-bellied White-eye Grosbeak Starling Zosterops chloris Scissiostrum dubium Theobroma cacao cacao agroforestry cacao diseases cacao harvest cacao pests flying vertebrates aerial insectivores volant vertebrates logging breeding colony flower herbivory mesopredator release predatory insects ants spiders aphids Lepidoptera Coleoptera Lepidopteran larvae sustainable agriculture sustainable agroforestry management implications management recommendations applied managament extensive land use management landscape management conservation biodiversity-friendly dbh distance to forest forest proximity habitat conversion herbivory Indonesia insectivorous bats insectivorous birds invertebrates Central Sulawesi Napu valley leaf gleaning leaf herbivory pests insects phytophagous insects plant productivity remnant forest trees shade tree managament South-east Asia Paleotropics tropical landscape tree height landscape gradient local shade management Cacao Pod Borer Cherelle Wilt fruit abortion Helopeltis sulawesi deforestation habitat fragmentation land use intensification forest margin species critical decline predation experiment species-specific functions Southeast Asia biodiversity conservation mesopredators food security human welfare human well-being poverty alleviation poverty reduction smallholder cacao smallholder plantation cacao farmers sustainable cacao management landscape perspective landscape scale large scale field experiment long term field experiment human wellbeing economic importance economic value valuation ecosystem services landscape matrix yield quality yield quantity fruit productivity species identity species diversity species richness ecosystem functioning pesticide use chemical compounds complex food web tritrophic interactions wildlife-friendly rural livelihoods endangered bird species endangered bat species endemic endemic birds endemic bats beneficial impact IUCN Lore Lindu National Park bioindicator indicator species forest margin agricultural habitat agroforestry systems population density predation activity species activity cacao flower cacao pod Orthoptera linear mixed effect models Tukey Post Hoc Test AICc model selection generalized least squares restricted maximum likelihood model simplification species richness estimator species accumulation linear mixed-effects models Likelihood Ratio Test Maximum Likelihood nocturnal insects diurnal arthropods nocturnal arthropods diurnal insects landscape context Land- und Forstwirtschaft (PPN621302791)

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