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In this study, we propose a new three-parameter lifetime model based on the type-I half-logistic G family and the unit-Gompertz model, which we named the half-logistic unit Gompertz type-I distribution. The key feature of such a novel model is that it adds a new tuning parameter to the unit-Gompertz model using the type-I half-logistic family in order to make the unit-Gompertz model more flexible. Diagrams and numerical results are used to look at the new model's mathematical and statistical aspects. The efficiency of estimating the distribution parameters is measured using a variety of well-known classical methodologies, including Anderson–Darling, maximum likelihood, least squares, weighted least squares, right tail Anderson–Darling, and Cramer–von Mises estimation. Finally, using the maximum likelihood estimation method, the flexibility and ability of the proposed model are illustrated by means of re-analyzing two real datasets, and comparisons are provided with the fit accomplished by the unit-Gompertz, Kumaraswamy, unit-Weibull, and Kumaraswamy beta distributions for illustrative purposes. [ABSTRACT FROM AUTHOR]

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts. [ABSTRACT FROM AUTHOR]

Retrofit or replacement of few units in a subcritical facility may not only improve overall efficiency of conversion of energy in a power plant but also support sustainability issues. The primary objective of this article is to identify model parameters of a coal-fired integrated boiler and to present a comparative study on three different identification methods. This leads to select most suitable models that are applied for the developed model of the boiler of 210 MW coal-fired thermal power plants. The mathematical models of economizer, drum, and super-heater assembly are derived using mass balance and energy balance equations. The derived multi input–multi output model is then validated, and the model parameters are identified using three different identification methods namely nonlinear least square technique, maximum likelihood estimation, and expectation maximization algorithms. Identification of the plant model will essentially help to frame a good controller. In this article, parameter estimation has been carried out from real-time plant as it provides selective tool through quantitative comparative study of the three methods. The expectation maximization method has been found to provide suitable results compared to the other two methods. Model parameters of integrated boiler of a comprehensive structure have been obtained for the first time using expectation maximization method. [ABSTRACT FROM AUTHOR]

A new generalization of the Lindley distribution is recently proposed by Ghitany et al. [1] , called as the power Lindley distribution. Another generalization of the Lindley distribution was introduced by Nadarajah et al. [2] , named as the generalized Lindley distribution. This paper proposes a more generalization of the Lindley distribution which generalizes the two. We refer to this new generalization as the exponentiated power Lindley distribution. The new distribution is important since it contains as special sub-models some widely well-known distributions in addition to the above two models, such as the Lindley distribution among many others. It also provides more flexibility to analyze complex real data sets. We study some statistical properties for the new distribution. We discuss maximum likelihood estimation of the distribution parameters. Least square estimation is used to evaluate the parameters. Three algorithms are proposed for generating random data from the proposed distribution. An application of the model to a real data set is analyzed using the new distribution, which shows that the exponentiated power Lindley distribution can be used quite effectively in analyzing real lifetime data. [ABSTRACT FROM AUTHOR]

In this paper, a generalization of inverted exponential distribution is considered as a lifetime model [A.M. Abouammoh and A.M. Alshingiti,Reliability estimation of generalized inverted exponential distribution, J. Statist. Comput. Simul. 79(11) (2009), pp. 1301–1315]. Its reliability characteristics and important distributional properties are discussed. Maximum likelihood estimation of the two parameters involved along with reliability and failure rate functions are derived. The method of least square estimation of parameters is also studied here. In view of cost and time constraints, type II progressively right censored sampling scheme has been used. For illustration of the performance of the estimates, a Monte Carlo simulation study is carried out. Finally, a real data example is given to show the practical applications of the paper. [ABSTRACT FROM AUTHOR]

This paper introduces a transmuted modified Weibull distribution as an important competitive model which contains eleven life time distributions as special cases. We generalized the three parameter modified Weibull distribution using the quadratic rank transmutation map studied by Shaw et al. [12] to develop a transmuted modified Weibull distribution. The properties of the transmuted modified Weibull distribution are discussed. Least square estimation is used to evaluate the parameters. Explicit expressions are derived for the quantiles. We derive the moments and examine the order statistics. We propose the method of maximum likelihood for estimating the model parameters and obtain the observed information matrix. This model is capable of modeling of various shapes of aging and failure criteria. [ABSTRACT FROM AUTHOR]

Abstract: under a set of assumptions, Maximum likelihood estimation method is analyzed and simulated to make wireless sensor network''s localization more accurate. The result shows that, the range error between the anchor node (we call it the reference node) and the unknown node contained in the nth equation (we call it the reference equation) and the position of the reference node has significant impact on the node localization. The result of the simulation shows that if the range error contained in the reference equation can be reduced more, and the position of the reference node is appropriate, the localization error will be smaller. [Copyright &y& Elsevier]

A low-complexity semi-blind joint channel estimation and data detection scheme is proposed for space-time shift keying (STSK) based multiple-input multiple-output systems. The minimum number of STSK training blocks, which is related to the number of transmitter antennas, is first utilized to provide a rough initial least square channel estimate (LSCE). Then low-complexity single-stream maximum likelihood (ML) data detection is carried out based on the initial LSCE and the detected data are employed to refine the decision-directed LSCE. It is demonstrated that a few iterations are sufficient to approach the optimal ML detection performance obtained with the perfect channel state information. [ABSTRACT FROM PUBLISHER]

Abstract In target node localization problem, conventional methods based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and values of its parameters specific for an environment. This limits the conventional localization system to be set up quickly and effectively due to a necessary pre-measurement step to determine both the channel model and the values of its parameters. To address the limitation, a twostage iterative algorithm which allows to localize a target node without any prior knowledge of the parameter values has been propose. Each stage of the algorithm can be implemented using different estimation methods, such as maximum likelihood (ML) and least square (LS) estimation which provides four different combinations. To determine the best combination, the location estimation performance for all four combinations is evaluated using experimental data collected in measurement campaigns on various indoor locations. The results reveal that the combination of ML estimation method implemented in both stages provides the best location estimation accuracy and the fastest convergence rate. [ABSTRACT FROM AUTHOR]