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The purpose of this study is to build a high accuracy thrust model under various small turbojet engine shaft speeds by using robust, ordinary, linear and nonlinear least squares estimation methods for target drone applications.

The dynamic shaft speeds from the test experiment of a target drone engine is conducted. Then, thrust values are calculated. Based on these, the engine thrust is modeled by robust linear and nonlinear equations. The models are benefited from quadratic, power and various series expansion functions with several coefficients to optimize the model parameters.

The error terms and accuracy of the model are given using sum of squared errors, root mean square error (RMSE) and R-squared (R²) error definitions. Based on the multiple analyses, it is seen that the RMSE values are no more than 17.7539 and the best obtained result for robust least squares estimation is 15.0086 for linear at all cases. Furthermore, the R² value is found to be 0.9996 as the highest with the nonlinear Fourier series expansion model.

The motivation behind this paper is to propose robust nonlinear thrust models based on power, Fourier and various series expansion functions for dynamic shaft speeds from the test experiments.

The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.

The error of Jacchia-Roberts atmospheric density model is expressed as an objective function about temperature parameters. The estimation of parameter corrections is a typical nonlinear least-squares problem. Three algorithms for nonlinear least-squares problems, Gauss–Newton (G-N), damped Gauss–Newton (damped G-N) and Levenberg–Marquardt (L-M) algorithms, are adopted to estimate temperature parameter corrections of Jacchia-Roberts for model calibration.

The results show that G-N algorithm is not convergent at some sampling points. The main reason is the nonlinear relationship between Jacchia-Roberts and its temperature parameters. Damped G-N and L-M algorithms are both convergent at all sampling points. G-N, damped G-N and L-M algorithms reduce the root mean square error of Jacchia-Roberts from 20.4% to 9.3%, 9.4% and 9.4%, respectively. The average iterations of G-N, damped G-N and L-M algorithms are 3.0, 2.8 and 2.9, respectively.

This study is expected to provide a guidance for the selection of nonlinear least-squares estimation methods in atmospheric density model calibration.

The study analyses the performance of three typical nonlinear least-squares estimation methods in the calibration of atmospheric density model. The non-convergent phenomenon of G-N algorithm is discovered and explained. Damped G-N and L-M algorithms are more suitable for the nonlinear least-squares problems in model calibration than G-N algorithm and the first two algorithms have slightly fewer iterations.

In the last two decades there has been considerable progress made in the development of alternative estimation techniques to ordinary least squares (OLS) regression. The search for alternative estimators has no doubt been motivated by the observance of erratic OLS estimator behavior in cases where there are too few observations, multicollinearity problems, or simply “information‐poor” data sets. Imprecise and unreliable OLS coefficient estimates have been the result.

The main purpose of this paper is to estimate the resistance and inductor in the RL electrical circuit when these are unavailable or missing data that it is a concern in electrical engineering. The input voltage is assumed to be corrupted by the noise and the current is observed at discrete time points.

The authors propose a computationally efficient framework for parameters estimation using least square estimator and Bayesian Monte Carlo scheme.

The explicit formulas for least square estimator are derived and the strong consistency of resistance estimator is verified when inductor is a known parameter, then Bayesian estimation of parameters governed by using Markov chain Monte Carlo methods. The applicability of the results is demonstrated by using numerical examples. Several numerical results and figures are presented via Matlab and R programming to illustrate the performance of the estimators.

The paper can be used in various types of electrical engineering real time projects. The projects include electrical circuits, electrical machines theory and drives, especially when the parameters are uncertain that it is a worry in electrical engineering.

To the author's best knowledge, least square and Bayesian estimation of resistance and inductor have not been studied before. The proposed model is nonlinear with respect to inductor (L); therefore the present work has fundamental difference in comparison with the similar models.

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised least‐squares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.

This study aims to examine the determinants of the allowance for loan losses (ALL) and loan loss provisions (LLP) for banks in the Middle East and North African (MENA) region using both a two-stage approach and simultaneous equation system to address the potential problem of estimation bias introduced by estimating the ALL and LLP separately. The paper also tests three competing hypotheses: the earnings management hypothesis, the capital management hypothesis, and the signaling hypothesis.

The authors adopt a simultaneous equation and three-stage approaches to test whether MENA banks jointly determine LLP and ALL and the determinants of the two accounts. The sample consists of all available electronic data for 75 banks (451 bank-year observations) in nine MENA countries over the period 2000-2008.

Evidence suggests that the two accounts are jointly determined. The results support the earnings management hypothesis – meaning that MENA banks have engaged in year-to-year income smoothing. The authors also find that LLP and ALL provide signals about future earnings.

The authors acknowledge that the LLP account is only one of many accounts on the income statement that could be used for signaling or to manage earnings, and that the ALL is one of several accounts that could be used for signaling, earnings or capital management. Future studies could examine other accruals for their role in managing earnings, signaling and capital.

The results indicate that bank managers use LLP and ALL accounts to manage earnings management, policy makers may want to limit the ability of banks to manipulate earnings.

Prior research on the loan loss accounting practices has been based on single equation models of the determinants of LLP and ALL. An issue that has not been adequately addressed in this literature is that ALL and LLP may be interrelated and jointly determined by banks. If the two accounts are not independent of each other, failure to include one when estimating the other may lead to an omitted variable problem, while including both in the same equation induces a potential simultaneity bias. The study is the first empirical work examining whether ALL and LLP are jointly determined by banks. By jointly estimating LLP and ALL, the study permits an assessment of the magnitude of the potential error from adopting ordinary least squares estimation of a single equation model.

The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).

The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.

The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.

After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.