Abstract: Assessing several individuals intensively over time
yields intensive longitudinal data (ILD). Even though ILD provide
rich information, they also bring other data analytic challenges. One
of these is the increased occurrence of missingness with increased
study length, possibly under non-ignorable missingness scenarios.
Multiple imputation (MI) handles missing data by creating several
imputed data sets, and pooling the estimation results across imputed
data sets to yield final estimates for inferential purposes. In this
article, we introduce dynr.mi(), a function in the R package,
Dynamic Modeling in R (dynr). The package dynr provides a suite
of fast and accessible functions for estimating and visualizing the
results from fitting linear and nonlinear dynamic systems models in
discrete as well as continuous time. By integrating the estimation
functions in dynr and the MI procedures available from the R
package, Multivariate Imputation by Chained Equations (MICE), the
dynr.mi() routine is designed to handle possibly non-ignorable
missingness in the dependent variables and/or covariates in a
user-specified dynamic systems model via MI, with convergence
diagnostic check. We utilized dynr.mi() to examine, in the context
of a vector autoregressive model, the relationships among individuals’
ambulatory physiological measures, and self-report affect valence
and arousal. The results from MI were compared to those from
listwise deletion of entries with missingness in the covariates.
When we determined the number of iterations based on the
convergence diagnostics available from dynr.mi(), differences in
the statistical significance of the covariate parameters were observed
between the listwise deletion and MI approaches. These results
underscore the importance of considering diagnostic information in
the implementation of MI procedures.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.
Abstract: This study examines whether the Taiwan’s public debt is sustainable utilizing an unrestricted two-regime threshold autoregressive (TAR) model with an autoregressive unit root. The empirical results show that Taiwan’s public debt appears as a nonlinear series and is stationary in regime 1 but not in regime 2. This result implies that while Taiwan’s public debt was mostly sustainable over the 1996 to 2013 period examined in the study, it may no longer be sustainable in the most recent two years as the public debt ratio has increased cumulatively to 3.618%.
Abstract: The purpose of this paper is to analyze the influence and relative share of underwriting risks in explaining the variation in insurance cycles in subsequent periods. Through the insurance contracts they underwrite, insurance companies assume risks. Underwriting risks include pricing risk, reserve risk, reinsurance risk and occurrence risk. These risks pose major risks for property and liability insurers, and therefore their impact on the insurance cycle is important. The main goal of this paper is to determine the relative proportion of underwriting risks in explaining the variation of insurance cycle. In order to fulfill the main goal of the paper vector autoregressive model, VAR, will be applied.
Abstract: Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector autoregressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is Neural networks using Nelson-Siegel estimation of yield curves.
Abstract: In this paper we present a Adaptive Neuro-Fuzzy
System (ANFIS) with inputs the lagged dependent variable for the
prediction of Gross domestic Product growth rate in six countries.
We compare the results with those of Autoregressive (AR) model.
We conclude that the forecasting performance of neuro-fuzzy-system
in the out-of-sample period is much more superior and can be a very
useful alternative tool used by the national statistical services and the
banking and finance industry.
Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: Periodicities in the environmetric time series can be
idyllically assessed by utilizing periodic models. In this
communication fugitive emission of gases from open sewer channel
Lyari which follows periodic behaviour are approximated by
employing periodic autoregressive model of order p. The orders of
periodic model for each season are selected through the examination
of periodic partial autocorrelation or information criteria. The
parameters for the selected order of season are estimated individually
for each emitted air toxin. Subsequently, adequacies of fitted models
are established by examining the properties of the residual for each
season. These models are beneficial for schemer and administrative
bodies for the improvement of implemented policies to surmount
future environmental problems.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: This paper deals with heterogeneous autoregressive
models of realized volatility (HAR-RV models) on high-frequency
data of stock indices in the USA. Its aim is to capture the behavior of
three groups of market participants trading on a daily, weekly and
monthly basis and assess their role in predicting the daily realized
volatility. The benefits of this work lies mainly in the application of
heterogeneous autoregressive models of realized volatility on stock
indices in the USA with a special aim to analyze an impact of the
global financial crisis on applied models forecasting performance.
We use three data sets, the first one from the period before the global
financial crisis occurred in the years 2006-2007, the second one from
the period when the global financial crisis fully hit the U.S. financial
market in 2008-2009 years, and the last period was defined over
2010-2011 years. The model output indicates that estimated realized
volatility in the market is very much determined by daily traders and
in some cases excludes the impact of those market participants who
trade on monthly basis.
Abstract: The hydrologic time series data display periodic
structure and periodic autoregressive process receives considerable
attention in modeling of such series. In this communication long
term record of monthly waste flow of Lyari river is utilized to
quantify by using PAR modeling technique. The parameters of
model are estimated by using Frances & Paap methodology. This
study shows that periodic autoregressive model of order 2 is the most
parsimonious model for assessing periodicity in waste flow of the
river. A careful statistical analysis of residuals of PAR (2) model is
used for establishing goodness of fit. The forecast by using proposed
model confirms significance and effectiveness of the model.
Abstract: In this paper we present, propose and examine
additional membership functions for the Smoothing Transition
Autoregressive (STAR) models. More specifically, we present the
tangent hyperbolic, Gaussian and Generalized bell functions.
Because Smoothing Transition Autoregressive (STAR) models
follow fuzzy logic approach, more fuzzy membership functions
should be tested. Furthermore, fuzzy rules can be incorporated or
other training or computational methods can be applied as the error
backpropagation or genetic algorithm instead to nonlinear squares.
We examine two macroeconomic variables of US economy, the
inflation rate and the 6-monthly treasury bills interest rates.
Abstract: Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.
Abstract: In this paper we present an autoregressive model with
neural networks modeling and standard error backpropagation
algorithm training optimization in order to predict the gross domestic
product (GDP) growth rate of four countries. Specifically we propose
a kind of weighted regression, which can be used for econometric
purposes, where the initial inputs are multiplied by the neural
networks final optimum weights from input-hidden layer after the
training process. The forecasts are compared with those of the
ordinary autoregressive model and we conclude that the proposed
regression-s forecasting results outperform significant those of
autoregressive model in the out-of-sample period. The idea behind
this approach is to propose a parametric regression with weighted
variables in order to test for the statistical significance and the
magnitude of the estimated autoregressive coefficients and
simultaneously to estimate the forecasts.