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Projeção de inflação no Brasil utilizando dados agregados e desagregados: um teste de poder preditivo por horizonte de tempoCarlos, Thiago Carlomagno 14 August 2012 (has links)
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Previous issue date: 2012-08-14 / This work has aim to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation. We will compare forecasting models using disaggregated and aggregated data from IPCA over twelve months ahead. We used IPCA in a monthly basis, over the period between January 1996 to March 2012. Out-ofsample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by SARIMA using X-12 ARIMA software provided by US Census Bureau, and will have different levels of disaggregation from IPCA as groups (9) and items (52), as well as disaggregation with more economic sense used by Brazilian Central Bank as: services, monitored prices, food and industrials; durables, non-durables, semi durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, space-estate structural models (Kalman Filter) and Markovswitching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set, introduced by Hansen, Lunde and Nason (2010), and by Dielbod Mariano (1995), in which we founded evidences of gain in accuracy in models with more disaggregation than aggregates models. / O trabalho tem como objetivo comparar a eficácia das diferentes metodologias de projeção de inflação aplicadas ao Brasil. Serão comparados modelos de projeção que utilizam os dados agregados e desagregados do IPCA em um horizonte de até doze meses à frente. Foi utilizado o IPCA na base mensal, com início em janeiro de 1996 e fim em março de 2012. A análise fora da amostra foi feita para o período entre janeiro de 2008 e março de 2012. Os modelos desagregados serão estimados por SARIMA, pelo software X-12 ARIMA disponibilizado pelo US Census Bureau, e terão as aberturas do IPCA de grupos (9) e itens (52), assim como aberturas com sentido mais econômico utilizadas pelo Banco Central do Brasil como: serviços, administrados, alimentos e industrializados; duráveis, não duráveis, semiduráveis, serviços e administrados. Os modelos agregados serão estimados por técnicas como SARIMA, modelos estruturais em espaço-estado (Filtro de Kalman) e Markov-switching. Os modelos serão comparados pela técnica de seleção de modelo Model Confidence Set, introduzida por Hansen, Lunde e Nason (2010), e Dielbod e Mariano (1995), no qual encontramos evidências de ganhos de desempenho nas projeções dos modelos mais desagregados em relação aos modelos agregados.
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An Analysis of Mark-Recapture Data from Coded Wire Tagging of Hatchery Raised Salmon Using Log-Linear Models and Graphics / An Analysis of Mark-Recapture DataGreen, Philip 09 1900 (has links)
In this report mark-recapture data are analyzed with the use of weighted log-linear models, mosaics, and computer drawings of fish. The data are from salmon hatcheries, subsequent returns to the hatcheries and commercial catches of salmon. The log-linear models were used to study the effects of several variables on catches and returns. It is shown that these variables may have opposite effects depending on the brood year of the fish, that hatchery returns and commercial catches do not respond in the same way to the variables, and that more research is needed to determine the causes of the brood year differences. The mosaics and fish drawings were used to study the migration of the salmon in the ocean. The results confirm that chinook salmon decrease their food intake during the return trip to the hatchery, and they are consistent with theories of ocean migration of other species of salmon. / Thesis / Master of Science (MSc)
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Simulation and characterisation of a concentrated solar power plant / Coenraad Josephus NelNel, Coenraad Josephus January 2015 (has links)
Concentrated solar power (CSP) is an efficient means of renewable energy that makes use of solar
radiation to produce electricity instead of making use of conventional fossil fuel techniques such as
burning coal. The aim of this study is the simulation and characterisation of a CSP plant in order to
gain a better understanding of the dominant plant dynamics. Due to the nature of the study, the
dissertation is divided into two main parts namely the simulation of a CSP plant model and the
characterisation of the plant model.
Modelling the CSP plant takes the form of developing an accurate Flownex® model of a 40 MW
combined cycle CSP plant. The model includes thermal energy storage as well as making use of a
duct burner. The Flownex® model is based on an existing TRNSYS model of the same plant. The
Flownex® model is verified and validated, by making use of a bottom-up approach, to ensure that
the developed model is in fact correct.
The characterisation part of this dissertation involves evaluating the dynamic responses unique to
that of a CSP plant as stated in the literature. This involves evaluating the dominant dynamic
behaviour, the presence of resonant and anti-resonant modes found within the control bandwidth,
and the change in the dynamics of the plant as the plants’ operating points change throughout the
day.
Once the developed model is validated, characterisation in the form of evaluating the open loop
local linear models of the plant is implemented. In order to do so, these models are developed
based on model identification processes, which include the use of system identification software
such as Matlab® SID Toolbox®.
The dominant dynamic behaviour of the plant model, obtained from the developed local linear
models, represents that of an over damped second order system that changes as the operating
points of the plant change; with the models’ time responses and the bandwidth decreasing and
increasing respectively as the thermal energy inputs to the plant increases. The frequency
response of the developed local linear models also illustrates the presence of resonant and antiresonant
modes found within the control bandwidth of the solar collector field’s temperature
response. These modes however are not found to be present in the mechanical power output
response of the plant.
The use of adaptive control, such as feedforward and gain-scheduled controllers, for the plant
should be developed to compensate for the dynamic behaviours associated with that of a CSP
plant. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015
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Simulation and characterisation of a concentrated solar power plant / Coenraad Josephus NelNel, Coenraad Josephus January 2015 (has links)
Concentrated solar power (CSP) is an efficient means of renewable energy that makes use of solar
radiation to produce electricity instead of making use of conventional fossil fuel techniques such as
burning coal. The aim of this study is the simulation and characterisation of a CSP plant in order to
gain a better understanding of the dominant plant dynamics. Due to the nature of the study, the
dissertation is divided into two main parts namely the simulation of a CSP plant model and the
characterisation of the plant model.
Modelling the CSP plant takes the form of developing an accurate Flownex® model of a 40 MW
combined cycle CSP plant. The model includes thermal energy storage as well as making use of a
duct burner. The Flownex® model is based on an existing TRNSYS model of the same plant. The
Flownex® model is verified and validated, by making use of a bottom-up approach, to ensure that
the developed model is in fact correct.
The characterisation part of this dissertation involves evaluating the dynamic responses unique to
that of a CSP plant as stated in the literature. This involves evaluating the dominant dynamic
behaviour, the presence of resonant and anti-resonant modes found within the control bandwidth,
and the change in the dynamics of the plant as the plants’ operating points change throughout the
day.
Once the developed model is validated, characterisation in the form of evaluating the open loop
local linear models of the plant is implemented. In order to do so, these models are developed
based on model identification processes, which include the use of system identification software
such as Matlab® SID Toolbox®.
The dominant dynamic behaviour of the plant model, obtained from the developed local linear
models, represents that of an over damped second order system that changes as the operating
points of the plant change; with the models’ time responses and the bandwidth decreasing and
increasing respectively as the thermal energy inputs to the plant increases. The frequency
response of the developed local linear models also illustrates the presence of resonant and antiresonant
modes found within the control bandwidth of the solar collector field’s temperature
response. These modes however are not found to be present in the mechanical power output
response of the plant.
The use of adaptive control, such as feedforward and gain-scheduled controllers, for the plant
should be developed to compensate for the dynamic behaviours associated with that of a CSP
plant. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015
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信用風險評估方法之研究 : Log-linear models運用於西藥零售商之實證研究李文福, LI, WEN-FU Unknown Date (has links)
第一章 緒論
第一節 研究背景
第二節 研究問題
第三節 研究目的
第四節 名詞定義及研究範圍
第二章 評估信用風險之文獻參考
第三章 研究設計
第一節 研究架構與變數
第二節 抽樣設計
第三節 資料分析方法
第四章 資料分析與研究結果
第一節 企業主持人執業資格、經驗、地緣關係與信用風險之關係。
第二節 各項特質對信用風險之預測能力。
第五章 結論與建議
附錄 Log-Iknear Models 統計技術簡介。
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The Decomposition of Tree-Ring Series for Environmental StudiesCook, Edward R. January 1987 (has links)
Signal extraction in tree-ring research is considered as a general time series decomposition problem. A linear aggregate model for a hypothetical ring-width series is proposed, which allows the problem to be reduced to the estimation and extraction of five discrete classes of signals. These classes represent the signals due to trend, climate, endogenous disturbance, exogenous disturbance, and random error. For each class of signal, some mathematical/statistical techniques of estimation are described and reviewed. Except for the exogenous disturbance signal, the techniques only require information contained within the ring-width series, themselves. A unified mathematical framework for solving this decomposition problem has not yet been explicitly formulated. However, the general applicability of ARMA time series models to this problem and the power and flexibility of state space modelling suggest that these techniques will provide the closest thing to a unified framework in the future.
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Statistické modely pro kapitálové modely pojišťoven / Statistical models for capital models of insurance companiesŠvagerková, Lýdia January 2011 (has links)
This work deals with the topic of lapse rate modelling in the field of Life Insurance. First, the theoretical apparatus is established: the linear models and their extension, generalized linear models. Furthermore, we describe the process of model selection and evaluation. In the second part of this work we describe the influence of various individual as well as macroeconomical parameters on the lapse rate. We summarize the findings of previous works in this field. The last part introduces models in statistical software R based on generalized linear models and describes the process of their selection and evaluation. Outputs from these models are interpreted and compared to the ratio analysis results.
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Gaining Insight With Recursive Partitioning Of Generalized Linear ModelsRusch, Thomas, Zeileis, Achim 06 1900 (has links) (PDF)
Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles.
Then, usually, a constant in every partition is fitted. While this is a simple and
intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and
independent variables may look. Or it may be that a certain model is assumed or of
interest and there is a number of candidate variables that may non-linearily give rise to
different model parameter values.
We present an approach that combines generalized linear models with recursive partitioning
that offers enhanced interpretability of classical trees as well as providing an
explorative way to assess a candidate variable's influence on a parametric model.
This method conducts recursive partitioning of a the generalized linear model by
(1) fitting the model to the data set, (2) testing for parameter instability over a set of
partitioning variables, (3) splitting the data set with respect to the variable associated with
the highest instability. The outcome is a tree where each terminal node is associated with a generalized linear model.
We will show the methods versatility and suitability to gain additional insight
into the relationship of dependent and independent variables by two examples, modelling
voting behaviour and a failure model for debt amortization. / Series: Research Report Series / Department of Statistics and Mathematics
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Chasin’ Tail in Southern Alabama: Delineating Programmed and Stimulus-driven Grooming in Odocoileus virginianusHeine, Kyle 11 August 2015 (has links)
This study examined variation in ectoparasite density and grooming behavior of naturally occurring white-tailed deer (Odocoileus virginianus) in southwest Alabama. Stimulus-driven grooming as well as the intraspecific body size and vigilance principles of programmed grooming were tested. During the rut, males had a higher average tick (Ixodidae) density than females and exhibited complete separation of tick parasitism between non-rutting and rutting periods, supporting the vigilance principle. Stimulus-driven grooming was supported, as both fawns and yearlings had significantly higher fly (Hippoboscidae) and combined fly/tick densities than adults, and fawns oral groomed at a significantly higher rate than adults, even in the absence of allogrooming. Programmed and stimulus-driven grooming of deer examined in this study were not mutually exclusive but ectoparasite dependent.
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Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancerZhai, Jing, Hsu, Chiu-Hsieh, Daye, Z. John 25 January 2017 (has links)
Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software. Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade. Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves.
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