7,270 research outputs found
A note on prediction bias for state space models with estimated parameters
This paper aims to discuss some problems on state space models with estimated parameters. While existing research focus on the prediction mean squared error, this work presents some results on bias propagation into forecast and filter predictions when the mean vector of the state is taking with an estimation bias, namely, non recursive analytical expression for them. In particular, it is discussed the impact of mean bias in invariant state space models
A Mixed-effect State Space Model to Environmental Data
This work presents some common issues in the statistical analysis of time series of environmental area. The discussion and the presentation of solutions is raised by the study of a time series of the oxygen concentration variable in a water quality monitoring site in the river Vouga hydrological basin in Portugal. Issues such as trends, seasonality, temporal correlation and detection of change points are addressed
A comparison between single site modeling and multiple site modeling approaches using Kalman filtering
This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set
A Periodic State Space Model to Monthly Long-term Temperature Data
This work presents a periodic state space model to model monthly temperature data. Additionally, some issues are discussed, as the parameter estimation or the Kalman filter recursions adapted to a periodic model. This framework is applied to monthly long-term temperature time series of Lisbon
Dynamic linear modeling of homogenized monthly temperature in Lisbon
This chapter focuses on the statistical modeling of the homogenized monthly average temperature data of Lisbon from 1856 to 2008. An exploratory analysis was performed using linear regression models which indicates the need of considering the temporal dependency and some flexibility in the trend modeling. In order to incorporate the properties of the data it was adopted a dynamic linear models with a fixed effect component. The model was fitted by a two-step procedure which combines the least squares method and the maximum likelihood estimation in the state space framework. The results indicated an average increase of the homogenized monthly temperature in Lisbon in about 0.427oC per century, between 1856 to 2008. Additionally, smoother predictions of the stochastic slopes indicated that the rise of temperature moderately changes according to the month, higher linear increases occurred in the winter months and lower increases occurred in the summer months
Parametric imaging of FET PET using nonlinear based fitting
Tese de mestrado integrado em Engenharia Biomédica e Biofísica , apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2016A importância do uso de aminoácidos marcados com isótopos radioativos em estudos de Tomografia de Emissão de Positrões (PET) tem sido amplamente demonstrada. Dentro deste grupo de traçadores, a metionina marcada com 11C tem sido o mais estudado. No entanto, a curta semi-vida do radioisótopo 11C tem levado ao desenvolvimento de marcadores análogos. Os marcadores com o radioisótopo 18F revelam-se os mais promissores para deteção de tumores no cérebro. Mais especificamente, o marcador O-(2-18F-Fluoroetil)-L-tirosina (FET) provou ser de grande importância na determinação da dimensão de tumores cerebrais e dos locais onde realizar a biopsia, no planeamento do tratamento a aplicar, e na deteção de recorrências. Foi também demonstrado que a forma como o FET é metabolizado ao longo do tempo depende do grau do tumor em estudo. Em gliomas de alto grau (HGG), a taxa de captação do FET é caracterizada por um pico inicial, seguido de uma diminuição da captação de FET, enquanto que em gliomas de baixo-grau (LGG) a taxa de captação do marcador tem um aumento contínuo ao longo do tempo.
O presente estudo contou com 11 pacientes (3 mulheres, 8 homens, idade: 45 ± 15 anos) com tumores cerebrais primários não tratados confirmados por histologia. Seis pacientes foram diagnosticados com HGG, enquanto os restantes 5 foram diagnosticados com LGG. Os dados de PET foram adquiridos com o PET Insert do sistema híbrido Siemens 3T MR-BrainPET. As imagens foram segmentadas de forma a extrair apenas o volume correspondente ao tumor. Após a segmentação, calculou-se a média das curvas de tempo-atividade (TAC) dos volumes tumorais segmentados (STVs), e foram usados métodos de regressão linear e não linear para fazer o ajuste à TAC de cada volume. Para calcular os ajustes com o modelo linear, foram descartados os primeiros 5 minutos de aquisição. Os ajustes baseados na regressão não-linear foram aplicados à TAC correspondente à média entre os 2 e os 60 minutos de aquisição após a injeção. As imagens dos parâmetros foram calculadas a partir dos ajustes baseados na regressão não linear e aplicados a cada voxel. Foram testados três modelos não lineares diferentes: um modelo linear amortecido exponencialmente, um modelo linear amortecido exponencialmente e com um offset, e um modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada. Dos ajustes não lineares, foram extraídos dois parâmetros: a amplitude, A, e o parâmetro κ. De seguida, geraram-se as imagens dos parâmetros calculados sobre uma área tridimensional selecionada manualmente e contendo o tumor. Para tal, além dos três modelos não lineares, utilizou-se também o modelo linear, de modo a permitir uma comparação entre os diferentes métodos. No caso dos ajustes lineares, os parâmetros extraídos foram a ordenada na origem e o declive. Calcularam-se também as imagens dos parâmetros da regressão não linear usando o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada para a cabeça inteira.
Os modelos não-lineares foram mais precisos na reprodução das curvas de FET. Os modelos mais robustos foram os modelos lineares exponencialmente amortecidos sem offset. Nos ajustes aplicados à TAC média dos STVs, o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada provou ser o que reproduz mais precisamente os dados, com valores de 2 entre 0,94 e 1,00. O parâmetro A do modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada foi o único que revelou uma diferença significativa entre HGG e LGG (p-value= 0.04, α=0.05). Ao gerar imagens paramétricas com base nos ajustes aplicados a cada voxel, os modelos de regressão não-linear com 2 parâmetros tiveram o melhor desempenho, com valores de 2 perto de 1. Combinando as imagens do parâmetro amplitude e as imagens da atividade total ao longo do tempo, foi possível distinguir entre graus tumorais. Os LGGs assumem valores de amplitude próximos dos valores do tecido saudável à sua volta, e por isso “desaparecem” da imagem paramétrica da amplitude. No caso dos HGGs, a imagem da amplitude reproduz a atividade no tumor. Os ajustes realizados com base na regressão linear devolveram valores de 2 próximos de zero, quer no caso dos STVs, quer no cálculo das imagens paramétricas. A distinção entre HGG e LGG é possível com base nas imagens paramétricas do declive, com os LGGs a assumirem valores de declive superiores aos do tecido saudável adjacente. Com os HGGs, a situação é a oposta: os valores do declive no tumor são inferiores aos do tecido saudável que o rodeia. Em geral, os modelos não lineares reproduzem melhor os dados provenientes de FET PET, mas a distinção entre HGG e LGG baseada num parâmetro apenas é melhor conseguida através de regressão linear. No entanto, a distinção entre HGG e LGG também é possível analisando simultaneamente as imagens dos parâmetros A e κ.The importance of radiolabeled amino acids in Positron Emission Tomography (PET) imaging of the brain has been demonstrated by several studies. The most well studied amino acid tracer is 11C-metionine, but because of the short half-life of 11C, 18F-labeled amino acid analogues have been developed for tumour imaging. A number of studies have proven the importance of O-(2-18F-Fluoroethyl)-L-tyrosine (FET) in determining the extent of cerebral gliomas, biopsy guidance, treatment planning, and detecting recurrence of brain tumours. It was also demonstrated that dynamic changes of FET accumulation in gliomas are variable. High-grade gliomas (HGG) are characterized by an early peak, followed by decrease of FET uptake, whereas the uptake in low-grade gliomas (LGG) steadily increases.
Eleven patients (3 female, 8 male, age: 45±15 years) with untreated primary brain tumours and histopathologic confirmation were studied. Six patients had HGG, while the remaining 5 were diagnosed with LGG. PET acquisition was done with the PET Insert of a hybrid Siemens 3T MR-BrainPET system. For tumour volume fitting, a segmentation procedure was applied. After segmentation, the mean time-activity curve (TAC) of the segmented tumour volumes (STVs) was calculated. Linear and nonlinear regression were used to fit to the TAC of each volume. When performing the fits with the linear model, the first 5 minutes of acquisition were discarded. For the nonlinear regression, the fits were applied to the mean TAC from 2 to 60 minutes after injection. Parametric images were calculated based on nonlinear regression fitting of FET data in each voxel. Three different nonlinear models were tested: an exponentially damped linear model, an exponentially damped linear model with an offset, and an exponentially damped linear model with square-root time dependence. The considered nonlinear model parameters were amplitude, A, and κ. The parametric images of manually selected tridimensional volumes containing the tumour were generated. Linear regression based parametric images were also computed for comparison, and the assessed parameters were intercept and slope. Whole-head parametric images were calculated based on nonlinear regression fitting using the exponentially damped linear model with square-root time dependence.
Nonlinear regression models were more accurate at reproducing FET TAC characteristics. The most robust models are the exponentially damped linear models without offset. For mean TAC fitting, a model with square-root time dependence reproduced FET activity curves more accurately, with coefficient of determination (2) values between 0.94 and 1.00. The A parameter from the exponentially damped linear model with square-root time dependence was the only one significantly different between HGG and LGG (p-value= 0.04, α=0.05). When generating parametric images based on voxel-wise fit, the nonlinear regression models with 2 parameters performed the best, with 2 close to 1. Visual distinction between tumour grades was possible by comparing the amplitude images with the images of the summed activity across time. In the amplitude, LGGs take values similar to the ones of the surrounding background, thus disappearing from the image. On the other hand, HGGs amplitude images reproduce tumour uptake. Linear regression model fits returned 2 values that were close to zero in both mean TAC fitting, and parametric image calculation. Grade distinction was possible based on the slope parameter alone, with LGGs showing higher slope values than the neighbouring tissue, and HGGs showing lower slope values than their surroundings. In general, though nonlinear models reproduce FET time activity curves more accurately, the distinction between low-grade and high-grade tumours based on one parameter only is better achieved by using linear regression model fitting. However, a reliable differentiation seems to be possible with joint analysis of A and κ parametric images
Change point analysis in a state space framework to monthly temperature data in European cities
In this work, we present time series of monthly average temperatures in several Euro-
pean locations which were statistically analyzed using a state space approach, where it
is considered a model with a deterministic seasonal component and a stochastic trend.
The analysis of smoother prediction of the stochastic trend and its comparison in a tem-
poral viewpoint can reveal patterns about warming in Europe. The temperature rise
rates in Europe seem to have increased in the last decades when compared with longer
periods, hence a change point detection method is applied to the trend component in
order to identify these possible changes in the monthly temperature rise rates. The
adopted methodology pointed out, for most series a change point in the late eighties.publishe
Releases as adverts : product discovery in video-games
Consumers do not have perfect information about products. Therefore, product discovery
plays a key role. I use the market for video-games to estimate the causal effect of the release
of a new game on the performance of an older game by the same company. I find that, in the
weeks before the release, the old game underperforms, which is consistent with a substitution
effect between the old and the new game. In the weeks after the release, the old game
increases performance significantly, which suggests that there is a backwards information
spillover. This average treatment effect can be as high as 20% in the number of owners.
Overall, I find that the release of a new game serves as a advert for the old game.Os consumidores não têm informação perfeita sobre produtos, o que implica que a descoberta
de produtos é essencial. Usando o mercado de video-jogos, eu estimo o efeito causal
do lançamento de um novo jogo no sucesso de um jogo pré-existente, produzido pela mesma
empresa. Nas semanas antes do lançamento, o jogo pré-existente sofre em termos de número
de utilizadores, o que é consistente com um efeito de substituição entre os dois jogos. Nas
semanas após o lançamento, o jogo pré-existente melhora substancialmente em termos de
número de utilizadores, o que sugere que existe um efeito de transmissão de informação.
O efeito médio de tratamento pode chegar a 20%. Em suma, eu encontro evidência que o
lançamento de um novo jogo serve como publicidade para os jogos pré-existentes
METAL-BASED ADDITIVE MANUFACTURING: Evaluation of metallic parts produced with Additive Manufacturing Technology at YAZAKI Europe Limited
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