Abstract: In this paper usefulness of quasi-Newton iteration
procedure in parameters estimation of the conditional variance
equation within BHHH algorithm is presented. Analytical solution of
maximization of the likelihood function using first and second
derivatives is too complex when the variance is time-varying. The
advantage of BHHH algorithm in comparison to the other
optimization algorithms is that requires no third derivatives with
assured convergence. To simplify optimization procedure BHHH
algorithm uses the approximation of the matrix of second derivatives
according to information identity. However, parameters estimation in
a/symmetric GARCH(1,1) model assuming normal distribution of
returns is not that simple, i.e. it is difficult to solve it analytically.
Maximum of the likelihood function can be founded by iteration
procedure until no further increase can be found. Because the
solutions of the numerical optimization are very sensitive to the
initial values, GARCH(1,1) model starting parameters are defined.
The number of iterations can be reduced using starting values close
to the global maximum. Optimization procedure will be illustrated in
framework of modeling volatility on daily basis of the most liquid
stocks on Croatian capital market: Podravka stocks (food industry),
Petrokemija stocks (fertilizer industry) and Ericsson Nikola Tesla
stocks (information-s-communications industry).
Abstract: Saddlepoint approximations is one of the tools to obtain
an expressions for densities and distribution functions. We approximate
the densities of the observed gaps between the hypopnea events
using the Huzurbazar saddlepoint approximation. We demonstrate the
density of a maximum likelihood estimator in exponential families.
Abstract: The fine structure of supercavitation in the wake of a
symmetrical cylinder is studied with high-speed video cameras. The
flow is observed in a cavitation tunnel at the speed of 8m/sec when the
sidewall and the wake are partially filled with the massive cavitation
bubbles. The present experiment observed that a two-dimensional
ripple wave with a wave length of 0.3mm is propagated in a
downstream direction, and then abruptly increases to a thicker
three-dimensional layer. IR-photography recorded that the wakes
originated from the horseshoe vortexes alongside the cylinder. The
wake was developed to inside the dead water zone, which absorbed the
bubbly wake propelled from the separated vortices at the center of the
cylinder. A remote sensing classification technique (maximum most
likelihood) determined that the surface porosity was 0.2, and the mean
speed in the mixed wake was 7m/sec. To confirm the existence of
two-dimensional wave motions in the interface, the experiments were
conducted at a very low frequency, and showed similar gravity waves
in both the upper and lower interfaces.
Abstract: Throughput is an important measure of performance of production system. Analyzing and modeling of production throughput is complex in today-s dynamic production systems due to uncertainties of production system. The main reasons are that uncertainties are materialized when the production line faces changes in setup time, machinery break down, lead time of manufacturing, and scraps. Besides, demand changes are fluctuating from time to time for each product type. These uncertainties affect the production performance. This paper proposes Bayesian inference for throughput modeling under five production uncertainties. Bayesian model utilized prior distributions related to previous information about the uncertainties where likelihood distributions are associated to the observed data. Gibbs sampling algorithm as the robust procedure of Monte Carlo Markov chain was employed for sampling unknown parameters and estimating the posterior mean of uncertainties. The Bayesian model was validated with respect to convergence and efficiency of its outputs. The results presented that the proposed Bayesian models were capable to predict the production throughput with accuracy of 98.3%.
Abstract: We consider a heterogeneously mixing SIR stochastic
epidemic process in populations described by a general graph.
Likelihood theory is developed to facilitate statistic inference for the
parameters of the model under complete observation. We show that
these estimators are asymptotically Gaussian unbiased estimates by
using a martingale central limit theorem.
Abstract: In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: This paper proposes a smart design strategy for a sequential detector to reliably detect the primary user-s signal, especially in fast fading environments. We study the computation of the log-likelihood ratio for coping with a fast changing received signal and noise sample variances, which are considered random variables. First, we analyze the detectability of the conventional generalized log-likelihood ratio (GLLR) scheme when considering fast changing statistics of unknown parameters caused by fast fading effects. Secondly, we propose an efficient sensing algorithm for performing the sequential probability ratio test in a robust and efficient manner when the channel statistics are unknown. Finally, the proposed scheme is compared to the conventional method with simulation results with respect to the average number of samples required to reach a detection decision.
Abstract: In this paper, we apply and compare two generalized estimating equation approaches to the analysis of car breakdowns data in Mauritius. Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observation as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use the two types of quasi-likelihood estimation approaches to estimate the parameters of the model: marginal and joint generalized quasi-likelihood estimating equation approaches. Under-dispersion parameter is estimated to be around 2.14 justifying the appropriateness of Com-Poisson distribution in modelling underdispersed count responses recorded in this study.
Abstract: Retention in the IT profession is critical for
organizations to stay competitive and operate reliably in the dynamic
business environment. Most organizations rely on compensation and
rewards as primary tools to enhance retention of employees. In this
quantitative survey-based study conducted at a large global bank, we
analyze the perceptions of 575 information technology (IT) software
professionals in India and Malaysia and find that fairness of rewards
has very little impact on retention likelihood. It is far more important
to actively involve employees in organizational activities. In
addition, our findings indicate that involvement is far more important
than information flow: the typical organizational communication to
keep employees informed.
Abstract: In this research three methods of Maximum Likelihood, Mahalanobis Distance and Minimum Distance were analyzed in the Western part of Isfahan province in the Iran country. For this purpose, the IRS satellite images and various land preparation uses in region including rangelands, irrigation farming, dry farming, gardens and urban areas were separated and identified. In these methods, matrix error and Kappa index were calculated and accuracy of each method, based on percentages: 53.13, 56.64 and 48.44, were obtained respectively. Considering the low accuracy of these methods to separate land uses due to spread of the land uses, it-s suggested the visual interpretation of the map, to preparing the land use map in this region. The map prepared by visual interpretation is in high accuracy if it will be accompany with the visit of the region.
Abstract: SDMA (Space-Division Multiple Access) is a MIMO
(Multiple-Input and Multiple-Output) based wireless communication
network architecture which has the potential to significantly increase
the spectral efficiency and the system performance. The maximum
likelihood (ML) detection provides the optimal performance, but its
complexity increases exponentially with the constellation size of
modulation and number of users. The QR decomposition (QRD)
MUD can be a substitute to ML detection due its low complexity and
near optimal performance. The minimum mean-squared-error
(MMSE) multiuser detection (MUD) minimises the mean square
error (MSE), which may not give guarantee that the BER of the
system is also minimum. But the minimum bit error rate (MBER)
MUD performs better than the classic MMSE MUD in term of
minimum probability of error by directly minimising the BER cost
function. Also the MBER MUD is able to support more users than
the number of receiving antennas, whereas the rest of MUDs fail in
this scenario. In this paper the performance of various MUD
techniques is verified for the correlated MIMO channel models based
on IEEE 802.16n standard.
Abstract: Many single or multispan arch bridges are
strengthened with the addition of some kind of structural support
between adjacent arches of multispan or beside the arch barrel of a
single span to increase the strength of the overall structure. It was
traditionally formed by either placing loose rubble masonry blocks
between the arches and beside the arches or using mortar or concrete
to construct a more substantial structural bond between the spans. On
the other hand backing materials are present in some existing bridges.
Existing arch assessment procedures generally ignore the effects of
backing materials. In this paper an investigation of the effects of
backing on ratings for masonry arch bridges is carried out. It is
observed that increasing the overall lateral stability of the arch
system through the inclusion of structural backing results in an
enhanced failure load by reducing the likelihood of any tension
occurring at the top of the arch.
Abstract: In multi-parameter family of distributions, conditions
for a modified maximum likelihood estimator to be second order
admissible are given. Applying these results to the multi-parameter
logistic regression model, it is shown that the maximum likelihood
estimator is always second order inadmissible. Also, conditions for
the Berkson estimator to be second order admissible are given.
Abstract: The objective of this study is to introduce estimators to the parameters and survival function for Weibull distribution using three different methods, Maximum Likelihood estimation, Standard Bayes estimation and Modified Bayes estimation. We will then compared the three methods using simulation study to find the best one base on MPE and MSE.
Abstract: We identify clawback triggers from firms- proxy
statements (Form DEF 14A) and use the likelihood of restatements to
proxy for financial reporting quality. Based on a sample of 578 U.S.
firms that voluntarily adopt clawback provisions during 2003-2009,
when restatement-based triggers could be decomposed into two types:
fraud and unintentional error, and we do observe the evidence that
using fraud triggers is associated with high financial reporting quality.
The findings support that fraud triggers can enhance deterrent effect of
clawback provision by establishing a viable disincentive against fraud,
misconduct, and otherwise harmful acts. These results are robust to
controlling for the compensation components, to different sample
specifications and to a number of sensitivity.
Abstract: Distant-talking voice-based HCI system suffers from
performance degradation due to mismatch between the acoustic
speech (runtime) and the acoustic model (training). Mismatch is
caused by the change in the power of the speech signal as observed at
the microphones. This change is greatly influenced by the change in
distance, affecting speech dynamics inside the room before reaching
the microphones. Moreover, as the speech signal is reflected, its
acoustical characteristic is also altered by the room properties. In
general, power mismatch due to distance is a complex problem. This
paper presents a novel approach in dealing with distance-induced
mismatch by intelligently sensing instantaneous voice power variation
and compensating model parameters. First, the distant-talking speech
signal is processed through microphone array processing, and the
corresponding distance information is extracted. Distance-sensitive
Gaussian Mixture Models (GMMs), pre-trained to capture both
speech power and room property are used to predict the optimal
distance of the speech source. Consequently, pre-computed statistic
priors corresponding to the optimal distance is selected to correct
the statistics of the generic model which was frozen during training.
Thus, model combinatorics are post-conditioned to match the power
of instantaneous speech acoustics at runtime. This results to an
improved likelihood in predicting the correct speech command at
farther distances. We experiment using real data recorded inside two
rooms. Experimental evaluation shows voice recognition performance
using our method is more robust to the change in distance compared
to the conventional approach. In our experiment, under the most
acoustically challenging environment (i.e., Room 2: 2.5 meters), our
method achieved 24.2% improvement in recognition performance
against the best-performing conventional method.
Abstract: This study applied the Theory of Planned Behavior
model in predicting dietary behavior among Type 2 diabetics in a
Kenyan environment. The study was conducted for three months
within the diabetic clinic at Kisii Hospital in Nyanza Province in
Kenya and adopted sequential mixed methods design combing both
qualitative and quantitative phases. Qualitative data was analyzed
using grounded theory analysis method. Structural equation modeling
using maximum likelihood was used to analyze quantitative data.
The results based on the common fit indices revealed that the theory
of planned behavior fitted the data acceptably well among the Type 2
diabetes and within dietary behavior {χ2 = 223.3, df = 77, p = .02,
χ2/df = 2.9, n=237; TLI = .93; CFI =.91; RMSEA (90CI) = .090(.039,
.146)}. This implies that the Theory of Planned Behavior holds and
forms a framework for promoting dietary practice among Type 2
diabetics.
Abstract: Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observations as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use quasi-likelihood estimation approach to estimate the parameters of the model. Under-dispersion parameter is estimated to be 2.14 justifying the appropriateness of Com-Poisson distribution in modelling under-dispersed count responses recorded in this study.
Abstract: The Random Coefficient Dynamic Regression (RCDR)
model is to developed from Random Coefficient Autoregressive
(RCA) model and Autoregressive (AR) model. The RCDR model
is considered by adding exogenous variables to RCA model. In this
paper, the concept of the Maximum Likelihood (ML) method is used
to estimate the parameter of RCDR(1,1) model. Simulation results
have shown the AIC and BIC criterion to compare the performance of
the the RCDR(1,1) model. The variables as the stationary and weakly
stationary data are good estimates where the exogenous variables
are weakly stationary. However, the model selection indicated that
variables are nonstationarity data based on the stationary data of the
exogenous variables.