• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.697-704
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• 2012

The maximum likelihood(ML) estimation of the scale parameters of an exponential distribution based on progressive Type II censored samples is given. The sample is multiply censored (some middle observations being censored); however, the ML method does not admit explicit solutions. In this paper, we propose multiply progressive Type II censoring. This paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply progressive Type II censoring. The scale parameter is estimated by approximate ML methods that use two different Taylor series expansion types ($AMLE_I$, $AMLE_{II}$). We also obtain the maximum likelihood estimator(MLE) of the scale parameter under the proposed multiply progressive Type II censored samples. We compare the estimators in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20 and 40 and various censored schemes. The $AMLE_{II}$ is better than MLE and $AMLE_I$ in the sense of the MSE.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.225-234
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• 2008

Orthogonal Frequency Division Multiplexing (OFDM) systems utilizing direct conversion receiver suffer from frequency selective (FS) and frequency independent (FI) phase and gain imbalances caused by imperfect local oscillator and low pass filter. In this paper, we analyze the impacts of the transmit/receive IQ imbalances on the system and propose the estimation and compensation schemes for those imbalances. The preamble signals coded by Alamouti scheme in the frequency domain could be used in the estimation of relatively large IQ imbalances with FS and FI characteristics and the estimation results are used for the compensation of distortions caused by the FI and FS IQ imbalances. The optimal maximum likelihood (ML) receiver or suboptimal ordered successive interference cancallation (OSIC) receiver utilizing the estimation results show symbol error rate (SER) performance improvement compared to zero-forcing (ZF) technique due to diversity gain inherent in the frequency domain IQ imbalances combined with the frequency selective channels.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.291-301
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• 2017

It is well known in a small sample that the maximum likelihood (ML) approach for variance components in the general linear model yields estimates that are biased downward. The ML estimate of residual variance tends to be downwardly biased. The underestimation of residual variance, which has implications for the estimation of marginal effects and asymptotic standard error of estimates, seems to be more serious in some limited dependent variable models, as shown by some researchers. An alternative frequentist's approach may be restricted or residual maximum likelihood (REML), which accounts for the loss in degrees of freedom and gives an unbiased estimate of residual variance. In this situation, the REML estimator is derived in a censored regression model. A small sample the REML is shown to provide proper inference on regression coefficients.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4099-4113
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• 2022

In this paper, we propose improved maximum access delay time, noise variance, and power delay profile (PDP) estimation schemes for orthogonal frequency division multiplexing (OFDM) system in multipath fading channels. To this end, we adopt the approximate maximum likelihood (ML) estimation strategy. For the first step, the log-likelihood function (LLF) of the received OFDM symbols is derived by utilizing only the cyclic redundancy induced by cyclic prefix (CP) without additional information. Then, the set of the initial path powers is sub-optimally obtained to maximize the derived LLF. In the second step, we can select a subset of the initial path power set, i.e. the maximum access delay time, so as to maximize the modified LLF. Through numerical simulations, the benefit of the proposed method is verified by comparison with the existing methods in terms of normalized mean square error, erroneous detection, and good detection probabilities.

• Journal of Hydrospace Technology
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• v.2 no.2
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• pp.24-35
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• 1996

This paper describes parameter identification techniques formulated for the estimation of maneuvering coefficients of a submerged body. The first part of this paper is concerned with the identifiability of the system parameters. The relationship between a stochastic linear time-invariant system and the equivalent dynamic system is investigated. The second is concerned with the development of the numerically stable identification technique. Two identification techniques are tested; one is the ma7mum likelihood (ML) methods using the Holder & Mead simplex search method and using the modified Newton-Raphson method, and the other is the modified extended Kalman filter (MEKF) method with a square-root algorithm, which can improve the numerical accuracy of the extended Kalman filter. As a results, it is said that the equations of motion for a submerged body have higher probability to generate simultaneous drift phenomenon compared to general state equations and only the ML method using the Holder & Mead simplex search method and the MEKF method with a square-root algorithm gives acceptable results.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.103-109
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• 2006

OFDM system has a disadvantage of sensitiveness about the effect of the frequency offset caused by the discord of oscillators in transmitter and receiver. The frequency offset can be divided into integral part and decimal part. Although the frequency offset of integral part do not effect orthogonality between subcarriers, it makes the bit error probability become to 0.5 because of circular transient among transmitted data symbols. This paper proposes a new estimation scheme of the frequency offset of integral part by Maximum Likelihood (ML) demanding only one training symbol in multipath fading channel environment. This proposed scheme not only can reduce the number of training symbol but do not increase the complexity and it shows the better performance by simulation.

• Environmental and Resource Economics Review
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• v.21 no.4
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• pp.921-945
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• 2012

Conditional Logit (CL) model is widely used since its model estimation and interpretation of results of the model is relatively easy, on the other hand, it has the limit of preference heterogeneity of respondents being not fully considered. In this study we used the two models, Mixed Logit (ML) Model and Latent Class Model (LCM) to explain preference heterogeneity of respondents for protection for Boryeong Dam wetland. As a result of the examination for heterogeneity in Boryeong city and six metropolitan areas, we found there was significant difference between two regions. While there was explicit preference heterogeneity within respondents in Boryeong city, we found little heterogeneity within respondents in six metropolitan areas. Thus in the case of six metropolitan areas, CL model can be used for parameter estimation while in the case of Boryeong city, WTP estimates are based on parameter estimates from ML model to reflect the heterogeneity within respondents. Additionally, ML model with interaction and 2-class LCM for respondents in Boryeong city were used to explain the sources of the heterogeneity. The ML model with interaction has advantage of explaining individual unobserved heterogeneity. However The comarison between these two models reflects the fact that LCM provided added information that was not conveyed in the ML model with interaction. Thus, Preference heterogeneity within respondents in this study may be better explained by class level through LCM rather than indiviual level through ML model.