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In recent years, the application of resampling methods to dependent data, such as time series or spatial data, has been a growing field in the study of statistics. In this dissertation, we discuss two such applications. In spatial statistics, the reliability of Kriging prediction methods relies on the observations coming from an underlying Gaussian process. When the observed data set is not from a multivariate Gaussian distribution, but rather is a transformation of Gaussian data, Kriging methods can produce biased predictions. Bootstrap resampling methods present a potential bias correction. We propose a parametric bootstrap methodology for the calculation of either a multiplicative or additive bias correction factor when dealing with Trans-Gaussian data. Furthermore, we investigate the asymptotic properties of the new bootstrap based predictors. Finally, we present the results for both simulated and real world data. In time series analysis, the estimation of covariance parameters is often of utmost importance. Furthermore, the understanding of the distributional behavior of parameter estimates, particularly the variance, is useful but often difficult. Block bootstrap methods have been particularly useful in such analyses. We introduce a new procedure for the estimation of covariance parameters for replicated time series data.

Titel: Finansiering i mikroföretag - En kvantitativ studie inom mikroföretags sätt att finansiera sig   Författare: Daniel Johansson, Eva Näätänen, Vlad Ulrich   Handledare: Emil Numminen   Institution: Institutionen för industriell ekonomi, Blekinge Tekniska Högskola   Kurs: Kandidatarbete i Företagsekonomi, 15 högskolepoäng   Syfte: Syftet med denna studie är att undersöka hur tillgängligheten av finansieringsmedel påverkar finansieringen i svenska mikroföretag.   Metod: En kvantitativ studie har genomförts genom att data om 630 mikroföretag samlades via nätenkäter.   Slutsatser: Denna studie visar att svenska mikroföretag väljer sin finansiering enligt en förkortad pecking order. Ett finansiellt gap existerar mellan mikroföretag och finansiella institut i Sverige vilket ökar risken för mikroföretag att blir nekade extern finansiering som exempelvis banklån. Tillgänglighet av banklån påverkar i sin tur hur mikroföretag finansierar sig. Det finansiella gapet minskar under mikroföretagens livscykel och därmed ökar tillgängligheten av vissa externa finansieringsalternativ. / le: Financing of micro-enterprises - A quantitative study in micro-enterprises’ way of funding   Authors: Daniel Johansson, Eva Näätänen, Vlad Ulrich   Supervisor: Emil Numminen   Department: Department of Industrial Economics, Blekinge Institute of Technology   Course: Bachelor’s thesis in Business Administration, 15 credits   Purpose: The purpose of this study is to investigate how the availability of financial resources affects the financing of Swedish micro-enterprises.   Method: A quantitative study was carried out by collecting data about 630 micro-enterprises through online surveys.   Results: This study shows that Swedish micro-enterprises choose their financing in order with a truncated pecking order. A financial gap exists between micro-enterprises and financial institutions in Sweden that increases the risk for micro-enterprises to be denied external finance such as bank loans. Availability of bank loans in turn affects the financial decisions of the micro-enterprises. The financial gap decreases during the businesses' lifecycle and thus increases the availability of some external financing options.

It has long been argued that prosodic cues may facilitate syntax acquisition (e.g., Morgan, 1986). Previous studies have shown that infants are sensitive to violations of typical correlations between clause-final prosodic cues (Hirsh-Pasek et al., 1987) and that prosody facilitates memory for strings of words (Soderstrom et al., 2005). This dissertation broaches the question of whether children can use this information in syntax acquisition by asking if learners can use the prosodic correlates of clauses to locate syntactic constituents. One property of certain syntactic constituents in natural languages is that they can move, so learning of constituency was inferred if participants treated prosodically-grouped words as cohesive, moveable chunks. In Experiment 1, 19-month-olds were familiarized with sentences from an artificial grammar with either 1-clause or 2-clause prosody. The infants from the 2-clause group later recognized the prosodically-marked clauses when they had moved to a new position in the sentence and had a new acoustic contour. Adults in Experiment 2 showed similar learning, although their judgments also rely on recognition of perceptually-salient words at prosodic boundaries. Subsequent experiments explored the mechanisms underlying this prosodic bootstrapping by testing Japanese-acquiring infants on English-based stimuli (Experiment 3) and English-acquiring infants on Japanese-based stimuli (Experiment 4). Infants were able to locate constituent-like groups of words with both native and non-native prosody, suggesting that the acoustic correlates of prosody are sufficiently robust across languages that they can be used in early syntax acquisition without extensive exposure to language-specific prosodic features. On the other hand, adults (Experiment 5) are less flexible, and are only able to use prosody consistent with their native language, suggesting an age- or experience-related tuning of the prosodic perceptual mechanism. This dissertation supports prosody as an important cue that allows infants and young children to break into syntax even before they understand many words, and helps explain the rapid rate of syntax acquisition.

Inference in generalized linear mixed models (GLMM) remains a topic of debate. Baayen, Davidson, and Bates (2008) outlines criticism against conventional ways of performing inference for GLMMs. There are various alternatives proposed but lit- tle consistency is found on which is the most reasonable. Our focus is on assessing temporal trends for mainly ecological count data. That is, we hope to provide a prag- matic approach to Poisson GLMMs for ecological researchers within the statistical programming environment R. To achieve this, we start by providing a description of the selected estimation and inferential procedures. We then complete a large scale simulation to evaluate each of the estimation methods. We implement a power analy- sis to assess each of the selected inferential procedures. We then go on to apply these procedures to data sampled by The National Parks of Canada. Finally, we conclude by giving a summary of our ?ndings and outlying work for the future.

[pt] Esta tese investiga o modo como a criança, no início da aquisição do português como língua materna, identifica a presença de estruturas recursivas no sintagma complementizador. Assume-se que a criança identifica a informação gramatical com base nas interfaces entre a língua interna que se desenvolve na criança, e os sistemas envolvidos no desempenho linguístico. Recursividade é a propriedade do sistema linguístico que possibilita a geração de um número infinito de objetos sintáticos complexos a partir de um número finito de itens lexicais, por meio de um número finito e restrito de operações. Em línguas, tais como o português, pode haver recursividade no nó mais alto da hierarquia sintática (C - complementizador), o que possibilita a geração de orações completivas (OCs). OCs satisfazem os requisitos de verbos que subcategorizam um CP, ocupam a posição de complemento do verbo e podem ser introduzidas por um complementizador. O aporte teórico desta tese é uma teoria da aquisição da linguagem fundada na hipótese do bootstrapping fonológico (MORGAN e DEMUTH, 1996; CHRISTOPHE et al, 1997) aliada à concepção de língua apresentada no Programa Minimalista (PM) da Linguística Gerativista (CHOMSKY, 1995, 1999, 2005; HAUSER, CHOMSKY e FITCH, 2002), tal como é proposta nos termos do modelo procedimental de aquisição da linguagem em Corrêa (2009a; 2009b; 2011). São reportados dois grupos de experimentos, cada um centrado em um tipo de informação que pode servir de pista para a criança identificar a presença de estruturas recursivas, quais sejam: a forma fônica dos complementizadores e a estrutura da OC, respectivamente. Os resultados indicam que, embora não haja evidência conclusiva de que a criança seja sensível à presença do complementizador por suas propriedades fônicas, aos 12 meses, crianças parecem ser sensíveis à estrutura da completiva, o que sugere que reconhecem a possibilidade de C como nó recursivo. Ao reconhecer a estrutura da completiva, a criança vai além da interface fônica e pode dar início ao processamento de relações de subcategorização. / [en] This thesis investigates how children acquiring Brazilian Portuguese (BP) identify recursive structures in the complementizer phrase. It is assumed that children identify grammatical information based on the interfaces between the internal language and the systems related to linguistic performance. Recursion is a property of the language system which allows humans to build an infinite set of complex syntactic objects from a finite set of lexical items and syntactic operations. BP allows recursion in the highest node of the hierarchical structure (C - complementizer), building completive clauses. Structurally, completive clauses occupy the position of the complement of a verb and are introduced by a complementizer. The theoretical framework of this thesis is a procedural theory of language acquisition that reconciles the phonological bootstrapping hypothesis (MORGAN AND DEMUTH, 1996; CHRISTOPHE et al, 1997) and minimalist assumptions (CHOMSKY, 1995, 1999, 2005; HAUSER, CHOMSKY AND FITCH, 2002), as in Corrêa (2009a; 2009b; 2011). Two groups of experiments are reported, which focus on possible cues to the identification of a recursive structure, namely: the phonic form of complementizer and the structure of the completive clause. The results indicate that, although there is no conclusive evidence that children are sensitive to the presence of the complementizer due to its phonic properties, children, at 12 months, are sensitive to the structure of the completive clause, thereby suggesting that they go beyond the phonetic interface and start to be able to process subcategorization relations.

This paper exposes problems of the commonly used technique of splitting the available data in neural spatial interaction modelling into training, validation, and test sets that are held fixed and warns about drawing too strong conclusions from such static splits. Using a bootstrapping procedure, we compare the uncertainty in the solution stemming from the data splitting with model specific uncertainties such as parameter initialization. Utilizing the Austrian interregional telecommunication traffic data and the differential evolution method for solving the parameter estimation task for a fixed topology of the network model [ i.e. J = 9] this paper illustrates that the variation due to different resamplings is significantly larger than the variation due to different parameter initializations. This result implies that it is important to not over-interpret a model, estimated on one specific static split of the data. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

Bootstrapping techniques have the potential to accelerate the development of language technology resources. This is of specific importance in the developing world where language technology resources are scarce and linguistic diversity is high. In this thesis we analyse the pronunciation modelling task within a bootstrapping framework, as a case study in the bootstrapping of language technology resources. We analyse the grapheme-to-phoneme conversion task in the search for a grapheme-to-phoneme conversion algorithm that can be utilised during bootstrapping. We experiment with enhancements to the Dynamically Expanding Context algorithm and develop a new algorithm for grapheme-tophoneme rule extraction (Default & Refine) that utilises the concept of a ‘default phoneme’ to create a cascade of increasingly specialised rules. This algorithm displays a number of attractive properties including rapid learning, language independence, good asymptotic accuracy, robustness to noise, and the production of a compact rule set. In order to have greater flexibility with regard to the various heuristic choices made during rewrite rule extraction, we define a new theoretical framework for analysing instance-based learning of rewrite rule sets. We define the concept of minimal representation graphs, and discuss the utility of these graphs in obtaining the smallest possible rule set describing a given set of discrete training data. We develop an approach for the interactive creation of pronunciation models via bootstrapping, and implement this approach in a system that integrates various of the analysed grapheme-to-phoneme alignment and conversion algorithms. The focus of this work is on combining machine learning and human intervention in such a way as to minimise the amount of human effort required during bootstrapping, and a generic framework for the analysis of this process is defined. Practical tools that support the bootstrapping process are developed and the efficiency of the process is analysed from both a machine learning and a human factors perspective. We find that even linguistically untrained users can use the system to create electronic pronunciation dictionaries accurately, in a fraction of the time the traditional approach requires. We create new dictionaries in a number of languages (isiZulu, Afrikaans and Sepedi) and demonstrate the utility of these dictionaries by incorporating them in speech technology systems. / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

People detection has been an attractive technology in computer vision. There are many useful applications in our daily life, for instance, intelligent surveillance and driver assistance system. People detection is a challenging matter as people adopt a wide range of poses, wear diverse clothes, and are visible in different kind of backgrounds with significant changes in illumination. In this thesis, some advanced techniques and powerful tools are presented in order to design a robust people detection system. First a baseline model is implemented by combining the Histogram of Oriented Gradients descriptor and linear Support Vector Machines. This baseline model obtains a good performance on the well-known INRIA dataset. Second an advanced model is proposed which has a two-layer cascade framework that achieves both accurate detection and lower computational complexity. For the first layer, the baseline model is used as a filter to generate several candidates. In this procedure, most positive samples survived and the majority of negative samples are rejected according to a preset threshold. The second layer uses a more discriminative model. We combine the Variational Local Binary Patterns descriptor, and the Histogram of Oriented Gradients descriptor as a new discriminative feature. Furthermore multi-scale feature descriptors are used to improve the discriminative power of the Variational Local Binary Patterns feature. Then we perform Feature Selection using the Feature Generating Machine in order to generate a concise descriptor based on this concatenated feature. Moreover Histogram Intersection Kernel Support Vector Machines is employed as an efficient tool of classification. The bootstrapping algorithm is used in the training procedure to exploit the information of the dataset. Finally our approach has a good performance on the INRIA dataset, with results superior to the baseline model.

Tato diplomová práce se zabývá modelováním transmisního mechanismu v ČR. Hlavním cílem je zmapovat časovou strukturu reakcí makroekonomických veličin na izolované exogenní šoky při použití modelů vektorové autoregrese (VAR). Pro analýzu VAR modelů pomocí simulace Monte Carlo byla vytvořena aplikace fungující v prostředí Matlabu.