Journal articles: 'Cost Estimator'

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Hur, Sung-ho, and Yiza Srikanth Reddy. "Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control." Applied Sciences 11, no. 12 (June 18, 2021): 5661. http://dx.doi.org/10.3390/app11125661.

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The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for example, a LiDAR sensor, to be shared between multiple turbines in a cluster. One turbine in a cluster is equipped with a sensor and the remaining turbines are equipped with a nonlinear estimator that acts as a sensor, which significantly reduces the cost of sensors. The turbine with a sensor is used to train the estimator, which is based on an artificial neural network. The proposed method could be used to train the estimator to estimate various different variables; however, this study focuses on wind speed and aerodynamic torque. A new controller is also introduced that uses aerodynamic torque estimated by the neural network-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by a conventional aerodynamic torque estimator, demonstrating improved results.

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Oyerinde, Olutayo Oyeyemi, Adam Flizikowski, and Tomasz Marciniak. "Iterative Hard Thresholding with Combined Variable Step Size & Momentum-Based Estimator for Wireless Communication Systems with Dynamic Sparse Channels." Electronics 10, no. 7 (April 1, 2021): 842. http://dx.doi.org/10.3390/electronics10070842.

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The channel of the broadband wireless communications system can be modeled as a dynamic sparse channel. Such a channel is difficult to reconstruct by using linear channel estimators that are normally employed for dense channels’ estimation because of their lack of capacity to use the inherent channel’s sparsity. This paper focuses on reconstructing this type of time-varying sparse channel by extending a recently proposed dynamic channel estimator. Specifically, variable step size’s mechanism and variable momentum parameter are incorporated into traditional Iterative Hard Thresholding-based channel estimator to develop the proposed Iterative Hard Thresholding with Combined Variable Step Size and Momentum (IHT-wCVSSnM)-based estimator. Computer simulations carried out in the context of a wireless communication system operating in a dynamic sparse channel, show that the proposed IHT-wCVSSnM-based estimator performs better than all the other estimators significantly. However, the computational complexity cost of the proposed estimator is slightly higher than the closely performing channel estimator. Nevertheless, the inherent complexity cost of the proposed estimator could be compromised in a situation where the system’s performance is of higher priority when compared with the computational complexity cost.

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Kasie, Fentahun Moges, and Glen Bright. "Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process." Journal of Modelling in Management 16, no. 3 (January 18, 2021): 825–47. http://dx.doi.org/10.1108/jm2-05-2020-0123.

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Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

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Guo, Du, Ma, Huo, and Peng. "A Model for Animal Home Range Estimation Based on the Active Learning Method." ISPRS International Journal of Geo-Information 8, no. 11 (October 30, 2019): 490. http://dx.doi.org/10.3390/ijgi8110490.

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Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range estimator (DFHRE) is proposed in this study, based on the active learning method (ALM). The Euclidean distance is replaced by the cost distance-induced geodesic distance transformation to account for the effects of terrain and obstacles. Three datasets are used to verify the proposed method, and comparisons with the kernel density-based estimator (KDE) and the local convex hulls (LoCoH) estimators and the cross validation test indicate that the proposed estimator outperforms the KDE and the LoCoH estimators.

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Wade, Gary L., and William A. Thomas. "COMPUTER COST ESTIMATOR FOR LANDSCAPE INSTALLATION." HortScience 27, no. 11 (November 1992): 1175e—1175. http://dx.doi.org/10.21273/hortsci.27.11.1175e.

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Cost estimating and job bidding are among the most complex and time-consuming tasks of landscape professionals. A software package was developed to make cost estimating more accurate and efficient. HORT LAND, computer cost estimator for landscape installation, was developed for IBM compatible PC's using SuperCalc 5 spreadsheet software. The user builds a series of data bases, including an items listing of materials and equipment utilized in his operation along with their associated cost. Then, he defines a series of generic tasks, such as planting a 1-gallon size plant, and refers to the previous items list and associated code numbers for the materials and equipment necessary to install the plant. Once these initial data bases are constructed and saved, the user inputs a plant list, including size and price, then instructs the computer to translate the appropriate data from the initial data bases to arrive at a detailed listing of costs. The program then computes direct job cost and bid price, including overhead and profit.

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Munguía, J., J. Ciurana, and C. Riba. "Neural-network-based model for build-time estimation in selective laser sintering." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223, no. 8 (April 24, 2009): 995–1003. http://dx.doi.org/10.1243/09544054jem1324.

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Cost assessment for rapid manufacturing (RM) is highly dependent on time estimation. Total build time dictates most indirect costs for a given part, such as labour, machine costs, and overheads. A number of parametric and empirical time estimators exist; however, they normally account for error rates between 20 and 35 per cent which are then translated to inaccurate final cost estimations. The estimator presented herein is based on the ability of artificial neural networks (ANNs) to learn and adapt to different cases, so that the developed model is capable of providing accurate estimates regardless of machine type or model. A simulation is performed with MATLAB to compare existing approaches for cost/time estimation for selective laser sintering (SLS). Error rates observed from the model range from 2 to 15 per cent, which shows the validity and robustness of the proposed method.

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Khare, Brij, and Habib Rehman. "Modified chain regression type estimator for population mean in the presence of non- response." International Journal of Accounting and Economics Studies 3, no. 2 (November 27, 2015): 165. http://dx.doi.org/10.14419/ijaes.v3i2.5491.

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<p>A modified chain regression type estimator for population mean in the presence of non-response have been proposed replacing Hansen & Hurwitz (1946) estimator for population mean by Searls (1964) type improved estimator and using Hansen & Hurwitz (1946) estimator for based on available information comparing to the study character in the second phase sample. The expressions for MSE for fixed sample size and also fixed cost have been obtained. The empirical studies show that the proposed estimator is more efficient than the relevant estimators in the case of fixed sample size as well as for fixed cost.</p>

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Zhang, Dongmin, Qiang Song, Guanfeng Wang, and Chonghao Liu. "A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions." Applied Sciences 11, no. 6 (March 22, 2021): 2809. http://dx.doi.org/10.3390/app11062809.

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This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.

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DoĞanata, Yurdaer N., and Asser N. Tantawi. "A Video Server cost/performance Estimator tool." Multimedia Tools and Applications 1, no. 2 (June 1995): 185–202. http://dx.doi.org/10.1007/bf01215938.

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Ekung, Samuel, Adeniran Lashinde, and Emmanuel Adu. "Critical Risks to Construction Cost Estimation." Journal of Engineering, Project, and Production Management 11, no. 1 (January 1, 2021): 19–29. http://dx.doi.org/10.2478/jeppm-2021-0003.

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AbstractThe prevalence of cost overrun in project delivery suggests an acute dearth of inclusive understanding of the effect of risks on construction cost estimation. In aberrant to the generic assumptions, customary to inquiries in construction risk researches, this paper appraised critical construction estimating risks. The study evaluated the sources, frequency and significance of construction estimating risks, using data from a questionnaire survey of 206 quantity surveyors in Nigeria. The data were analysed using factor analysis, Fussy Set Theory, Terrell Transformation Index (TTI), and Kruskal Wallis H tests. The results showed that estimating risks are correlate seven principal sources, namely: estimating resources, construction knowledge, design information, economic condition, the expertise of estimator, geographic factor, cost data, and project factors (λ, > 0.70 <1.0). Twenty-nine risk factors likewise emerged critical construction estimation risks (TTI, 69-87 > 65 percent) and the top three were low construction knowledge, inaccurate cost information and changes in government regulations (factor scores > 0.60 > 0.50). The awareness and accurate assessment of these risks into project cost estimation would reduce cost overrun. The study, therefore, recommends synergies between projects’ internal/ external environments for proper scoping of these risks into project estimates.

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Wang, Xin, Zhi Yu, Le Yang, and Ji Li. "Design and Analysis of a Non-Iterative Estimator for Target Location in Multistatic Sonar Systems with Sensor Position Uncertainties." Mathematics 8, no. 1 (January 15, 2020): 129. http://dx.doi.org/10.3390/math8010129.

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Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid parameter estimation problem. Conventional gradient-based iterative estimators suffer from the problems of initialization difficulties and local convergence. Even if there is no problem with initialization and convergence, a large computational cost is required in most cases. In view of these drawbacks, we develop a computationally efficient non-iterative position/velocity estimator. The main numerical computation involved is the weighted least squares optimization, which makes the estimator computationally efficient. Parameter transformation, model linearization and two-stage processing are exploited to prevent the estimator from iterative computation. Through performance analysis and experimental verification, we find that the proposed estimator reaches the hybrid Cramér–Rao bound and has linear computational complexity.

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Mahmoud, Magdi S., and Peng Shi. "Optimal guaranteed cost filtering for Markovian jump discrete-time systems." Mathematical Problems in Engineering 2004, no. 1 (2004): 33–48. http://dx.doi.org/10.1155/s1024123x04108016.

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This paper develops a result on the design of robust steady-state estimator for a class of uncertain discrete-time systems with Markovian jump parameters. This result extends the steady-state Kalman filter to the case of norm-bounded time-varying uncertainties in the state and measurement equations as well as jumping parameters. We derive a linear state estimator such that the estimation-error covariance is guaranteed to lie within a certain bound for all admissible uncertainties. The solution is given in terms of a family of linear matrix inequalities (LMIs). A numerical example is included to illustrate the theory.

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Wei, Chao Yi, Xu Guang Li, Mei Zhi Xie, and Feng Yan Yi. "Soft Measurement Technology for Tractor-Semi Trailer." Applied Mechanics and Materials 494-495 (February 2014): 821–24. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.821.

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A three degree of freedom vehicle dynamic model of a tractor semi trailer is established and a simulation mode is set up with the Matlab/simulink software. Then, design a Kalman state estimator, using easily measured parameters to estimate the tractor side slip angle, trailer yaw rate those are difficult measured or measuring in high cost; the simulation show that: the estimated value coming from estimator agree well with the simulation measurements, and can improve the estimation accuracy by adjusting the process noise covariance matrix and measurement noise covariance matrix.

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Abbasi, Azhar Mehmood, and Muhammad Yousaf Shad. "Sensitive proportion in ranked set sampling." PLOS ONE 16, no. 8 (August 31, 2021): e0256699. http://dx.doi.org/10.1371/journal.pone.0256699.

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This paper considers the concomitant-based rank set sampling (CRSS) for estimation of the sensitive proportion. It is shown that CRSS procedure provides an unbiased estimator of the population sensitive proportion, and it is always more precise than corresponding sample sensitive proportion (Warner SL (1965)) that based on simple random sampling (SRS) without increasing sampling cost. Additionally, a new estimator based on ratio method is introduced using CRSS protocol, preserving the respondent’s confidentiality through a randomizing device. The numerical results of these estimators are obtained by using numerical integration technique. An application to real data is also given to support the methods.

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Pearce, Mark E., Earl T. Campbell, and Pieter Kok. "Optimal quantum metrology of distant black bodies." Quantum 1 (July 26, 2017): 21. http://dx.doi.org/10.22331/q-2017-07-26-21.

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Measurements of an object's temperature are important in many disciplines, from astronomy to engineering, as are estimates of an object's spatial configuration. We present the quantum optimal estimator for the temperature of a distant body based on the black body radiation received in the far-field. We also show how to perform separable quantum optimal estimates of the spatial configuration of a distant object, i.e. imaging. In doing so we necessarily deal with multi-parameter quantum estimation of incompatible observables, a problem that is poorly understood. We compare our optimal observables to the two mode analogue of lensed imaging and find that the latter is far from optimal, even when compared to measurements which are separable. To prove the optimality of the estimators we show that they minimise the cost function weighted by the quantum Fisher information---this is equivalent to maximising the average fidelity between the actual state and the estimated one.

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HIDAKA, SHOHEI. "Estimating the latent number of types in growing corpora with reduced cost–accuracy trade-off." Journal of Child Language 43, no. 1 (February 24, 2015): 107–34. http://dx.doi.org/10.1017/s0305000915000094.

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ABSTRACTThe number of unique words in children's speech is one of most basic statistics indicating their language development. We may, however, face difficulties when trying to accurately evaluate the number of unique words in a child's growing corpus over time with a limited sample size. This study proposes a novel technique to estimate the latent number of words from a series of words uttered by children. This technique utilizes statistical properties of the number of types as a function of the number of sampled tokens. We tested the practical effectiveness of the proposed method in the empirical data analysis of the cross-sectional and longitudinal samples. The converging empirical evidence indicates that the proposed estimator improves the accuracy of vocabulary size estimation over a set of existing estimators. Utilizing this efficient estimator, we propose a new sampling scheme for vocabulary assessment that has lower cost and higher accuracy compared to existing methods.

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Fan, Jianming, and Binqiang Xue. "Moving Horizon Estimation for Uncertain Networked Control Systems with Packet Loss." Mathematical Problems in Engineering 2020 (July 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/9875891.

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This paper studies a moving horizon estimation approach to solve the constrained state estimation problem for uncertain networked systems with random packet loss. The system model error range is known, and the packet loss phenomena are modeled by a binary switching random sequence. Taking the model error, the packet loss, the system constraints, and the network transmission noise into account, a time-varying weight matrix is obtained by solving a least-square problem. Then, a robust moving horizon estimator is designed to estimate the system state by minimizing an optimization problem with an arrival cost function. The proposed estimator ensures that the optimal estimated state can be obtained in the worst case. Furthermore, the asymptotic convergence of the estimator is analyzed and some sufficient conditions for convergence are given. Finally, the validity of the proposed approach can be demonstrated by numerical simulations.

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Qiao, Gang, Zeeshan Babar, Lu Ma, and Xue Li. "Cost Function based Soft Feedback Iterative Channel Estimation in OFDM Underwater Acoustic Communication." Infocommunications journal, no. 1 (2019): 29–37. http://dx.doi.org/10.36244/icj.2019.1.4.

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Underwater Acoustic (UWA) communication is mainly characterized by bandwidth limited complex UWA channels. Orthogonal Frequency Division Multiplexing (OFDM) solves the bandwidth problem and an efficient channel estimation scheme estimates the channel parameters. Iterative channel estimation refines the channel estimation by reducing the number of pilots and coupling the channel estimator with channel decoder. This paper proposes an iterative receiver for OFDM UWA communication, based on a novel cost function threshold driven soft decision feedback iterative channel technique. The receiver exploits orthogonal matching pursuit (OMP) channel estimation and low density parity check (LDPC) coding techniques after comparing different channel estimation and coding schemes. The performance of the proposed receiver is verified by simulations as well as sea experiments. Furthermore, the proposed iterative receiver is compared with other non-iterative and soft decision feedback iterative receivers.

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Sun, Ji, and Guoliang Li. "An end-to-end learning-based cost estimator." Proceedings of the VLDB Endowment 13, no. 3 (November 2019): 307–19. http://dx.doi.org/10.14778/3368289.3368296.

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Kim, Minyoung. "Cost-Sensitive Estimation of ARMA Models for Financial Asset Return Data." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/232184.

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The autoregressive moving average (ARMA) model is a simple but powerful model in financial engineering to represent time-series with long-range statistical dependency. However, the traditional maximum likelihood (ML) estimator aims to minimize a loss function that is inherently symmetric due to Gaussianity. The consequence is that when the data of interest are asset returns, and the main goal is to maximize profit by accurate forecasting, the ML objective may be less appropriate potentially leading to a suboptimal solution. Rather, it is more reasonable to adopt an asymmetric loss where the model's prediction, as long as it is in the same direction as the true return, is penalized less than the prediction in the opposite direction. We propose a quite sensible asymmetric cost-sensitive loss function and incorporate it into the ARMA model estimation. On the online portfolio selection problem with real stock return data, we demonstrate that the investment strategy based on predictions by the proposed estimator can be significantly more profitable than the traditional ML estimator.

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Xu, Yu, Wenda Sun, and Ping Li. "A Miniature Integrated Navigation System for Rotary-Wing Unmanned Aerial Vehicles." International Journal of Aerospace Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/748940.

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This paper presents the development of a low cost miniature navigation system for autonomous flying rotary-wing unmanned aerial vehicles (UAVs). The system incorporates measurements from a low cost single point GPS and a triaxial solid state inertial/magnetic sensor unit. The navigation algorithm is composed of three modules running on a microcontroller: the sensor calibration module, the attitude estimator, and the velocity and position estimator. The sensor calibration module relies on a recursive least square based ellipsoid hypothesis calibration algorithm to estimate biases and scale factors of accelerometers and magnetometers without any additional calibration equipment. The attitude estimator is a low computational linear attitude fusion algorithm that effectively incorporates high frequency components of gyros and low frequency components of accelerometers and magnetometers to guarantee both accuracy and bandwidth of attitude estimation. The velocity and position estimator uses two cascaded complementary filters which fuse translational acceleration, GPS velocity, and position to improve the bandwidth of velocity and position. The designed navigation system is feasible for miniature UAVs due to its low cost, simplicity, miniaturization, and guaranteed estimation errors. Both ground tests and autonomous flight tests of miniature unmanned helicopter and quadrotor have shown the effectiveness of the proposed system, demonstrating its promise in UAV systems.

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Lederer, Albert L., and Jayesh Prasad. "Information Systems Software Cost Estimating: A Current Assessment." Journal of Information Technology 8, no. 1 (March 1993): 22–33. http://dx.doi.org/10.1177/026839629300800104.

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A study of information systems managers and other information systems professionals at 112 different organizations confirmed that information systems software cost estimating is an important concern. Subjects reported the completion of only one of every four systems development projects within their estimates. According to them, the major cause of inaccurate estimates was changes in user requirements. Organizations using sophisticated cost estimating software packages were less successful at preventing large cost overruns than organizations not using them. However, the use of the estimator as system developer, the careful monitoring of systems development projects, and the inclusion in performance evaluations of success in meeting estimates were associated with more accurate cost estimating.

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Ginestet, Cedric E., Richard Emsley, and Sabine Landau. "Stein-like estimators for causal mediation analysis in randomized trials." Statistical Methods in Medical Research 29, no. 4 (June 7, 2019): 1129–48. http://dx.doi.org/10.1177/0962280219852388.

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Causal mediation analysis aims to estimate natural direct and natural indirect effects under clearly specified assumptions. Traditional mediation analysis based on Ordinary Least Squares assumes an absence of unmeasured causes to the putative mediator and outcome. When these assumptions cannot be justified, instrumental variable estimators can be used in order to produce an asymptotically unbiased estimator of the mediator-outcome link, commonly referred to as a Two-Stage Least Squares estimator. Such bias removal, however, comes at the cost of variance inflation. A Semi-Parametric Stein-Like estimator has been proposed in the literature that strikes a natural trade-off between the unbiasedness of the Two-Stage Least Squares procedure and the relatively small variance of the Ordinary Least Squares estimator. The Semi-Parametric Stein-Like estimator has the advantage of allowing for a direct estimation of its shrinkage parameter. In this paper, we demonstrate how this Stein-like estimator can be implemented in the context of the estimation of natural direct and natural indirect effects of treatments in randomized controlled trials. The performance of the competing methods is studied in a simulation study, in which both the strength of hidden confounding and the strength of the instruments are independently varied. These considerations are motivated by a trial in mental health, evaluating the impact of a primary care-based intervention to reduce depression in the elderly.

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Xiao, Min, Ting Chen, Kunpeng Huang, and Ruixing Ming. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing." Journal of Systems Science and Information 8, no. 6 (December 1, 2020): 549–64. http://dx.doi.org/10.21078/jssi-2020-549-16.

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Abstract Detecting differential expression of genes in genom research (e.g., 2019-nCoV) is not uncommon, due to the cost only small sample is employed to estimate a large number of variances (or their inverse) of variables simultaneously. However, the commonly used approaches perform unreliable. Borrowing information across different variables or priori information of variables, shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic. In this paper, we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution. Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well. In addition, application comparison and real data analysis indicate that the proposed estimator also works well.

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Vovk, Serhii. "PARAMETER ESTIMATION FOR COMPLICATED NOISE ENVIRONMENT." System technologies 6, no. 125 (December 27, 2019): 15–25. http://dx.doi.org/10.34185/1562-9945-6-125-2019-02.

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For a complicated noise environment the use of M-estimator faces a problem of choosing a cost function yielding the best solution. To solve this problem it is proposed to use a superset of cost functions. The superset capabilities provide constructing a parameter estimation method for complicated noise environment. It consists in tuning the generalized maximum likelihood estimation to the current noise environment by setting values of three free superset parameters related to the scale, the tail heaviness and the form of noise distribution, as well as to the anomaly values that presence in data. In general case, this method requires to solve the optimization problem with a non-unimodal objective function, and it can be mostly implemented by using the zero-order optimization methods. However, if the noise environment has known statistics, the proposed method leads to the optimal estimation. If the noise environment is complicated or does not have a complete statistics, the proposed method leads to the more effective estimates comparing to those of mean, median, myriad and meridian estimators. Numerical simulations confirmed the method performance.

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Todini, E. "Influence of parameter estimation uncertainty in Kriging: Part 1 - Theoretical Development." Hydrology and Earth System Sciences 5, no. 2 (June 30, 2001): 215–23. http://dx.doi.org/10.5194/hess-5-215-2001.

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Abstract. This paper deals with a theoretical approach to assessing the effects of parameter estimation uncertainty both on Kriging estimates and on their estimated error variance. Although a comprehensive treatment of parameter estimation uncertainty is covered by full Bayesian Kriging at the cost of extensive numerical integration, the proposed approach has a wide field of application, given its relative simplicity. The approach is based upon a truncated Taylor expansion approximation and, within the limits of the proposed approximation, the conventional Kriging estimates are shown to be biased for all variograms, the bias depending upon the second order derivatives with respect to the parameters times the variance-covariance matrix of the parameter estimates. A new Maximum Likelihood (ML) estimator for semi-variogram parameters in ordinary Kriging, based upon the assumption of a multi-normal distribution of the Kriging cross-validation errors, is introduced as a mean for the estimation of the parameter variance-covariance matrix. Keywords: Kriging, maximum likelihood, parameter estimation, uncertainty

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Mehdi Fateh, Mohammad, Siamak Azargoshasb, and Saeed Khorashadizadeh. "Model-free discrete control for robot manipulators using a fuzzy estimator." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 3 (April 29, 2014): 1051–67. http://dx.doi.org/10.1108/compel-05-2013-0185.

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Purpose – Discrete control of robot manipulators with uncertain model is the purpose of this paper. Design/methodology/approach – The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm. Findings – The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control. Originality/value – A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.

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STAUB–FRENCH, SHERYL, MARTIN FISCHER, JOHN KUNZ, KOS ISHII, and BOYD PAULSON. "A feature ontology to support construction cost estimating." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 2 (May 2003): 133–54. http://dx.doi.org/10.1017/s0890060403172034.

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Construction cost estimators are confronted with the challenging task of estimating the cost of constructing one of a kind facilities. They must first recognize the design conditions of the facility design that are important (i.e., incur a cost) and then determine how the design conditions affect the cost of construction. Current product models of facility designs explicitly represent components, attributes of components, and relationships between components. These designer-focused product models do not represent many of the cost-driving features of building product models, such as penetrations and component similarity. Previous research efforts identify many of the different features that affect construction costs, but they do not provide a formal and general way for practitioners to represent the features they care about according to their preferences. This paper presents the formal ontology we developed to represent construction knowledge about the cost-driving features of building product models. The ontology formalizes three classes of features, defines the attributes and functions of each feature type, and represents the relationships between the features explicitly. The descriptive semantics of the model allow estimators to represent their varied preferences for naming features, specifying features that result from component intersections and the similarity of components, and grouping features that affect a specific construction domain. A software prototype that implements the ontology enables estimators to transform designer-focused product models into estimator-focused, feature-based product models. Our tests show that estimators are able to generate and maintain cost estimates more accurately, consistently, and expeditiously with feature-based product models than with industry standard product models.

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Niasar, Abolfazl Halvaei, Hossein Rahimi Khoei, Mahdi Zolfaghari, and Hassan Moghbeli. "Artificial Neural Network Based Sensorless Vector Control of Induction Motor Drive." Applied Mechanics and Materials 704 (December 2014): 325–28. http://dx.doi.org/10.4028/www.scientific.net/amm.704.325.

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Controlled induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. For these speed sensorless AC drive system, it is key to realize speed estimation accurately. This paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab. Simulation result shows a good performance of speed estimator. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to resistances of stator variations.

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Gilley, Otis, and Kelley Pace. "A Hybrid Cost and Market-Based Estimator for Appraisal." Journal of Real Estate Research 5, no. 1 (January 1, 1990): 75–88. http://dx.doi.org/10.1080/10835547.1990.12090605.

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Rich, Benjamin, Erica E. M. Moodie, and David A. Stephens. "Influence Re-weighted G-Estimation." International Journal of Biostatistics 12, no. 1 (May 1, 2016): 157–77. http://dx.doi.org/10.1515/ijb-2015-0015.

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Abstract Individualized medicine is an area that is growing, both in clinical and statistical settings, where in the latter, personalized treatment strategies are often referred to as dynamic treatment regimens. Estimation of the optimal dynamic treatment regime has focused primarily on semi-parametric approaches, some of which are said to be doubly robust in that they give rise to consistent estimators provided at least one of two models is correctly specified. In particular, the locally efficient doubly robust g-estimation is robust to misspecification of the treatment-free outcome model so long as the propensity model is specified correctly, at the cost of an increase in variability. In this paper, we propose data-adaptive weighting schemes that serve to decrease the impact of influential points and thus stabilize the estimator. In doing so, we provide a doubly robust g-estimator that is also robust in the sense of Hampel (15).

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Muhammad, Yousaf Shad, Saima Khan, Ijaz Hussain, Alaa Mohamd Shoukry, Sadaf Shamsuddin, and Showkat Gani. "Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection." Scientific Programming 2019 (February 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/7193726.

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In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.

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Cui, Shitong, Le Liu, Wei Xing, and Xudong Zhao. "Periodic Event-Triggered Estimation for Networked Control Systems." Electronics 10, no. 18 (September 10, 2021): 2215. http://dx.doi.org/10.3390/electronics10182215.

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This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator.

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Saleemi, J. "An estimation of cost-based market liquidity from daily high, low and close prices." Finance, Markets and Valuation 6, no. 2 (2020): 1–11. http://dx.doi.org/10.46503/vutl1758.

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In the literature of asset pricing, this paper introduces a new method to estimate the cost-based market liquidity (CBML), that is, the bid-ask spread. The proposed model of spread proxy positively correlates with the examined low-frequency spread proxies for a larger dataset. The introduced approach provides potential implications in important aspects. Unlike in the Roll bid-ask spread model and the CHL bid-ask estimator, the CBML model consistently estimates market liquidity and trading cost for the entire dataset. Additionally, the CBML estimator steadily measures positive spreads, unlike in the CS bid-ask spread model. The construction of the proposed approach is not computationally intensive and can be considered for distinct studies at both market and firm levels.

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Vishwakarma, Gajendra Kumar, and Sayed Mohammed Zeeshan. "Generalized Ratio-cum-Product Estimator for Finite Population Mean under Two-Phase Sampling Scheme." Journal of Modern Applied Statistical Methods 19, no. 1 (June 8, 2021): 2–16. http://dx.doi.org/10.22237/jmasm/1608553320.

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A method to lower the MSE of a proposed estimator relative to the MSE of the linear regression estimator under two-phase sampling scheme is developed. Estimators are developed to estimate the mean of the variate under study with the help of auxiliary variate (which are unknown but it can be accessed conveniently and economically). The mean square errors equations are obtained for the proposed estimators. In addition, optimal sample sizes are obtained under the given cost function. The comparison study has been done to set up conditions for which developed estimators are more effective than other estimators with novelty. The empirical study is also performed to supplement the claim that the developed estimators are more efficient.

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Alaghbari, Khaled Abdulaziz, Lim Heng Siong, and Alan W. C. Tan. "Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 962–78. http://dx.doi.org/10.1108/compel-08-2014-0199.

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Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

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Elshall and Ye. "Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection." Water 11, no. 8 (July 30, 2019): 1579. http://dx.doi.org/10.3390/w11081579.

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Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

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Meng, Cheng, Xinlian Zhang, Jingyi Zhang, Wenxuan Zhong, and Ping Ma. "More efficient approximation of smoothing splines via space-filling basis selection." Biometrika 107, no. 3 (May 7, 2020): 723–35. http://dx.doi.org/10.1093/biomet/asaa019.

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Summary We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size $n$, the smoothing spline estimator can be expressed as a linear combination of $n$ basis functions, requiring $O(n^3)$ computational time when the number $d$ of predictors is two or more. Such a sizeable computational cost hinders the broad applicability of smoothing splines. In practice, the full-sample smoothing spline estimator can be approximated by an estimator based on $q$ randomly selected basis functions, resulting in a computational cost of $O(nq^2)$. It is known that these two estimators converge at the same rate when $q$ is of order $O\{n^{2/(pr+1)}\}$, where $p\in [1,2]$ depends on the true function and $r > 1$ depends on the type of spline. Such a $q$ is called the essential number of basis functions. In this article, we develop a more efficient basis selection method. By selecting basis functions corresponding to approximately equally spaced observations, the proposed method chooses a set of basis functions with great diversity. The asymptotic analysis shows that the proposed smoothing spline estimator can decrease $q$ to around $O\{n^{1/(pr+1)}\}$ when $d\leq pr+1$. Applications to synthetic and real-world datasets show that the proposed method leads to a smaller prediction error than other basis selection methods.

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ADJEI-FRIMPONG, KOFI, CHRISTOPHER GAN, and BAIDING HU. "EFFICIENCY AND COMPETITION IN THE GHANAIAN BANKING INDUSTRY: A PANEL GRANGER CAUSALITY APPROACH." Annals of Financial Economics 08, no. 01 (June 2013): 1350004. http://dx.doi.org/10.1142/s2010495213500048.

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This study examines the causal link between bank efficiency and bank competition. The study estimates bank competition with the Lerner index and bank cost efficiency with data envelopment analysis using annual data over the period 2001–2010. Using system generalized method of moments (system GMM) estimator, the results suggest that bank cost efficiency positively Granger-causes market power and hence causality negatively runs from bank cost efficiency to bank competition indicating that bank cost efficiency precedes bank competition. However, the reverse causality running from bank competition to bank cost efficiency is not supported.

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Bo Hu and Alex Z. Fu. "Predicting Utility for Joint Health States: A General Framework and a New Nonparametric Estimator." Medical Decision Making 30, no. 5 (July 19, 2010): E29—E39. http://dx.doi.org/10.1177/0272989x10374508.

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Measuring utility is important in clinical decision making and cost-effectiveness analysis because utilities are often used to compute quality-adjusted life expectancy, a metric used in measuring the effectiveness of health care programs and medical interventions. Predicting utility for joint health states has become an increasingly valuable research topic because of the aging of the population and the increasing prevalence of comorbidities. Although multiplicative, minimum, and additive estimators are commonly used in practice, research has shown that they are all biased. In this study, the authors propose a general framework for predicting utility for joint health states. This framework includes these 3 nonparametric estimators as special cases. A new simple nonparametric estimator, the adjusted decrement estimator, [Uij = Umin - Umin (1 - Ui )(1 - Uj )], is introduced under the proposed framework. When applied to 2 independent data sources, the new nonparametric estimator not only generated unbiased prediction of utilities for joint health states but also had the least root mean squared error and highest concordance when compared with other nonparametric and parametric estimators. Further research and validation of this new estimator are needed.

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Takenouchi, Takashi. "A Novel Parameter Estimation Method for Boltzmann Machines." Neural Computation 27, no. 11 (November 2015): 2423–46. http://dx.doi.org/10.1162/neco_a_00781.

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We propose a novel estimator for a specific class of probabilistic models on discrete spaces such as the Boltzmann machine. The proposed estimator is derived from minimization of a convex risk function and can be constructed without calculating the normalization constant, whose computational cost is exponential order. We investigate statistical properties of the proposed estimator such as consistency and asymptotic normality in the framework of the estimating function. Small experiments show that the proposed estimator can attain comparable performance to the maximum likelihood expectation at a much lower computational cost and is applicable to high-dimensional data.

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Sim, Hyo-Seon, and Key-Il Shin. "A Composite Estimator for Cut-off Sampling using Cost Function." Korean Journal of Applied Statistics 27, no. 1 (February 28, 2014): 43–59. http://dx.doi.org/10.5351/kjas.2014.27.1.043.

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Henshaw, Paul F., Stephan Cervi, and J. Alex McCorquodale. "Simple Cost Estimator for Environmental Dredging in the Great Lakes." Journal of Waterway, Port, Coastal, and Ocean Engineering 125, no. 5 (September 1999): 241–46. http://dx.doi.org/10.1061/(asce)0733-950x(1999)125:5(241).

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Wallentin, Erik. "Choice of the angler." Tourism Economics 22, no. 6 (September 21, 2016): 1338–51. http://dx.doi.org/10.5367/te.2015.0486.

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The author proposes a novel application for estimating demand for a single recreation site when data are limited. An aggregated zonal travel cost model is outlined and estimated in a panel setting using a negative binomial estimator with time-specific constants to capture unobserved time-varying quality attributes. The data are based on catch records for the Mörrum river salmon fishery, one of the most popular sport fishing destinations in Northern Europe. These are aggregated to the municipal level for the zonal estimation and give number of visits per municipality per week for the period 1999–2011. The results confirm the expected negative influence of travel cost on visits. Further, the inclusion of time-specific constants does not render all time-varying quality attributes insignificant.

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Lim, Brendon, Madhav P. Nepal, Martin Skitmore, and Bo Xiong. "Drivers of the accuracy of developers’ early stage cost estimates in residential construction." Journal of Financial Management of Property and Construction 21, no. 1 (April 4, 2016): 4–20. http://dx.doi.org/10.1108/jfmpc-01-2015-0002.

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Purpose – Preliminary cost estimates for construction projects are often the basis of financial feasibility and budgeting decisions in the early stages of planning and for effective project control, monitoring and execution. The purpose of this paper is to identify and better understand the cost drivers and factors that contribute to the accuracy of estimates in residential construction projects from the developers’ perspective. Design/methodology/approach – The paper uses a literature review to determine the drivers that affect the accuracy of developers’ early stage cost estimates and the factors influencing the construction costs of residential construction projects. It used cost variance data and other supporting documentation collected from two case study projects in South East Queensland, Australia, along with semi-structured interviews conducted with the practitioners involved. Findings – It is found that many cost drivers or factors of cost uncertainty identified in the literature for large-scale projects are not as apparent and relevant for developers’ small-scale residential construction projects. Specifically, the certainty and completeness of project-specific information, suitability of historical cost data, contingency allowances, methods of estimating and the estimator’s level of experience significantly affect the accuracy of cost estimates. Developers of small-scale residential projects use pre-established and suitably priced bills of quantities as the prime estimating method, which is considered to be the most efficient and accurate method for standard house designs. However, this method needs to be backed with the expertise and experience of the estimator. Originality/value – There is a lack of research on the accuracy of developers’ early stage cost estimates and the relevance and applicability of cost drivers and factors in the residential construction projects. This research has practical significance for improving the accuracy of such preliminary cost estimates.

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Bădin, Luiza, and Léopold Simar. "A BIAS-CORRECTED NONPARAMETRIC ENVELOPMENT ESTIMATOR OF FRONTIERS." Econometric Theory 25, no. 5 (October 2009): 1289–318. http://dx.doi.org/10.1017/s0266466609090513.

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In efficiency analysis, the production frontier is defined as the set of the most efficient alternatives among all possible combinations in the input-output space. The nonparametric envelopment estimators rely on the assumption that all the observations fall on the same side of the frontier. The free disposal hull (FDH) estimator of the attainable set is the smallest free disposal set covering all the observations. By construction, the FDH estimator is an inward-biased estimator of the frontier.The univariate extreme values representation of the FDH allows us to derive a bias-corrected estimator for the frontier. The presentation is based on a probabilistic formulation where the input-output pairs are realizations of independent random variables drawn from a joint distribution whose support is the production set. The bias-corrected estimator shares the asymptotic properties of the FDH estimator. But in finite samples, Monte Carlo experiments indicate that our bias-corrected estimator reduces significantly not only the bias of the FDH estimator but also its mean squared error, with no computational cost. The method is also illustrated with a real data example. A comparison with the parametric stochastic frontier indicates that the parametric approach can easily fail under wrong specification of the model.

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TOKUMOTO, SHUNSUKE, TADASHI DOHI, and WON YOUNG YUN. "BOOTSTRAP CONFIDENCE INTERVAL OF OPTIMAL AGE REPLACEMENT POLICY." International Journal of Reliability, Quality and Safety Engineering 21, no. 04 (August 2014): 1450018. http://dx.doi.org/10.1142/s0218539314500181.

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In this paper, we consider an interval estimation of the age replacement problem, where the underlying failure time probability is given by the two-parameter Weibull distribution. The parametric bootstrap is used to obtain the probability distribution of an estimator for the optimal age replacement time minimizing the expected cost per unit time in the steady state. We focus on both cases with complete and incomplete samples of failure time data, and calculate not only the higher moments of an estimator for the optimal age replacement time but also the confidence interval.

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Kiziltas, Semiha, Burcu Akinci, and Cleotilde Gonzalez. "Comparison of experienced and novice cost estimator behaviors in information pull and push methods." Canadian Journal of Civil Engineering 37, no. 2 (February 2010): 290–301. http://dx.doi.org/10.1139/l09-152.

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Construction cost estimators mainly use analogy-based decision-making processes while estimating activity production rates in new bids. They need to search information items that will help them in understanding the differences and similarities between the current and previously completed projects to select appropriate production rates and make necessary adjustments to the selected production rates. The objective of the study is to understand the effect of information pull–push methods on the behaviors of cost estimators when they refer to information items from historical sources in structuring their decisions. Eleven experienced estimators and 11 novice civil engineering candidates participated in an experiment involving card games, which simulated the information pull method, and in another experiment involving a prototype system, which simulated the information push method. Results showed that novices can behave like experienced estimators when information relevant to a decision is pushed to them. This result has implications for the design of information systems. Results show that it is possible to improve the estimates of novice estimators by providing them with decision support tools designed with the right information delivery method for their level of experience.

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Liu, Xiufang, Dianliang Deng, and Dehui Wang. "Estimating the quantile medical cost under time-dependent covariates and right censored time-to-event variable based on a state process." Statistical Methods in Medical Research 29, no. 8 (October 23, 2019): 2041–62. http://dx.doi.org/10.1177/0962280219882968.

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Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method.

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Tran, Tuan-Vu, and Edouard Nègre. "Efficient Estimator of Rotor Temperature Designing for Electric and Hybrid Powertrain Platform." Electronics 9, no. 7 (July 4, 2020): 1096. http://dx.doi.org/10.3390/electronics9071096.

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This paper presents an efficient method of estimation of rotor cage temperature for induction machine design, applied for electric and hybrid vehicles. This factor influences the torque produced by the induction machine with a field-oriented control algorithm. Equipping sensors to measure the temperature of a rotation component is expensive and is not representative of mass production. The approach of estimation of rotor cage temperature is based on the good knowledge of motor parameters and the estimation of the flux of the machine. For an accuracy inductance taking account of the saturation, the no-load test can be performed. The machine flux will be estimated taking account of the voltage drop of the system on the test-bench. The rapid prototyping in a real-time motor control platform will be presented that integrates this estimator of rotor temperature. We finally show the experimental testing results compared to the measurement of the rotor cage on a prototype asynchronous low-cost motor designing for battery electric city cars.

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Journal articles on the topic 'Cost Estimator'

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