140,127 research outputs found
Models Of The Intra-Regional Trade Influence On Economic Sustainable Development In Romania
This paper estimates the impact of trade between Romania and EU (export and imports) on sustainable development (defined through the following ratios: GDP growth and employment level) using dynamic forecasting and vector auto-regression (VAR) methods. Logistic regression forecasts the evolution of the GDP based on the evolution of exports and imports in the light of the Lisbon agenda, and the time horizon of 2010. Using data regarding intra-regional trade, the model may predict the GDP evolution, and, consequently, the economic sustainable development in Romania, as a new member of European Union.sustainable development intra-regional trade, economic integration, regression, model
Bayesian Fused Lasso regression for dynamic binary networks
We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online
Thompson Sampling in Dynamic Systems for Contextual Bandit Problems
We consider the multiarm bandit problems in the timevarying dynamic system
for rich structural features. For the nonlinear dynamic model, we propose the
approximate inference for the posterior distributions based on Laplace
Approximation. For the context bandit problems, Thompson Sampling is adopted
based on the underlying posterior distributions of the parameters. More
specifically, we introduce the discount decays on the previous samples impact
and analyze the different decay rates with the underlying sample dynamics.
Consequently, the exploration and exploitation is adaptively tradeoff according
to the dynamics in the system.Comment: 22 pages, 10 figure
A comparative analysis of non-linear techniques in South African stock selection
Includes bibliographical referencesForecasting stock performance has long been one of the primary objectives of financial practitioners. Literature has shown that the classical linear approach to modelling the interactions among company-specific factors and its stock market re- turns in time have become less suited for capturing the movements of the stock market. Hence, attempts to predict the performance of a stock have become associated with additional layers of complexity. This has led to the adoption of non-linear approaches to forecast stock performance. This dissertation explores the performance of some non-linear models in the South African market. These were classification and regression trees (CART), logistic regression and a random forest approach com- pared against a linear regression model. Moreover, a hybrid model between CART and logistic regression was considered. The models fell into two categories (i.e., static and dynamic models). Using a set of classification and portfolio performance metrics it was found that that a dynamic modelling approach outperformed a static approach. Overall, the logistic and linear regression models dominated in terms of performance against the tree-based models and hybrid approaches. The results also demonstrated that a hybrid approach offered an improvement over a stand-alone CART
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The Integration of Heterogeneous Open Source Software to Develop an Urban Simulation Model
Recent development of open source geospatial software offers new opportunities for the spatial analysis and urban modeling fields. The use of open source software enables analysts and modelers to build dedicated and advanced models through computer programing. However, many open source geospatial software usually provides building blocks for the static data management, analysis, and visualisation. Hence, development of dynamic simulation model with open source geospatial software is not yet fully fledged. The goals of this study are twofold. Firstly, it aims to develop a dynamic urban growth simulation model by using and integrating heterogeneous open source software. Secondly, by doing so, it aims to suggest a new way to use logistic regression model as a method for dynamic urban growth simulation. The research uses R and Processing to develop an urban growth simulation model. The former is a well-known open source statistical software, and the latter is an open source software for data visualisation. The integration of two open source software and the model development are carried out in a Java programming environment. The reason of such integration is to build a dynamic urban growth simulation from a conventional binominal logistic regression model. Binominal logistic regression is well-known method to calculate a certain choice probability, and it has often been used to analyse the possibility of future urban development. However, the result from such logistic regression by nature is stochastic and static. To make it as a method for urban growth simulation, what this research has done is the integration of following tasks: execution of logistic regression, extraction of coefficients from the result, calculation of development probability, iterative allocation of new development, and visualisation of such urban development. The model was applied to a case study area, Busan Metropolitan Area, Korea in order to examine its usability.It has produced statistically meaningful outcome, and the model shows a new way of developing dynamic urban simulation model. However, all data processing and manipulation is done in a separateGIS environment, and it is not integrated into the model. A tight-coupling with open source geospatial software could be a possible future research
State-space based mass event-history model I: many decision-making agents with one target
A dynamic decision-making system that includes a mass of indistinguishable
agents could manifest impressive heterogeneity. This kind of nonhomogeneity is
postulated to result from macroscopic behavioral tactics employed by almost all
involved agents. A State-Space Based (SSB) mass event-history model is
developed here to explore the potential existence of such macroscopic
behaviors. By imposing an unobserved internal state-space variable into the
system, each individual's event-history is made into a composition of a common
state duration and an individual specific time to action. With the common state
modeling of the macroscopic behavior, parametric statistical inferences are
derived under the current-status data structure and conditional independence
assumptions. Identifiability and computation related problems are also
addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB
mass event-history model is shown to be very distinct from a random effect
model via the Principle Component Analysis (PCA) in a numerical experiment.
Real data showing the mass invasion by two species of parasitic nematode into
two species of host larvae are also analyzed. The analysis results not only are
found coherent in the context of the biology of the nematode as a parasite, but
also include new quantitative interpretations.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS189 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Predicting time to graduation at a large enrollment American university
The time it takes a student to graduate with a university degree is mitigated
by a variety of factors such as their background, the academic performance at
university, and their integration into the social communities of the university
they attend. Different universities have different populations, student
services, instruction styles, and degree programs, however, they all collect
institutional data. This study presents data for 160,933 students attending a
large American research university. The data includes performance, enrollment,
demographics, and preparation features. Discrete time hazard models for the
time-to-graduation are presented in the context of Tinto's Theory of Drop Out.
Additionally, a novel machine learning method: gradient boosted trees, is
applied and compared to the typical maximum likelihood method. We demonstrate
that enrollment factors (such as changing a major) lead to greater increases in
model predictive performance of when a student graduates than performance
factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure
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