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Journal articles on the topic 'Multiple mean estimation'
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Green, Edwin J., William E. Strawderman, Ralph L. Amateis, and Gregory A. Reams. "Improved Estimation for Multiple Means with Heterogeneous Variances." Forest Science 51, no. 1 (February 1, 2005): 1–6. http://dx.doi.org/10.1093/forestscience/51.1.1.
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Abstract Two new estimators are presented for use in situations where simultaneous estimation of more than two sample means is required and alternative, possibly biased information is available. The new estimators are modifications to an older estimator by Green and Strawderman. The latter estimator assumed homogeneous variances, whereas the new ones are designed for the more usual case of heterogeneous variances among the sample means. In simulation experiments, the new estimators yielded superior performance to that of the ordinary sample mean vector (X). Surprisingly, the estimator designed to dominate X under precision-weighted loss was apparently the best estimator, even under nonprecision-weighted loss. Use of the new estimators should allow foresters to achieve considerable savings in estimation precision. FOR. SCI. 51(1):1–6.
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Liu, Huan Bin. "Additive-Accelerated Mean Regression Model for Multiple Type Recurrent Events." Advanced Engineering Forum 6-7 (September 2012): 93–96. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.93.
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Recurrent events data is often observed in applied research fields like biostatistics, clinical experiment, and so on. In this paper, an additive-accelerated mean regression model is established for multiple type recurrent events data, and the estimation methods of unknown parameter and non-parameter function based on the idea of estimating equation are given.
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Sedeeq, Bekhal Samad, Hogr Mohammed Qader, Azhy Akram Aziz, and Dlshad Mahmood Saleh. "Implementing a New Scale Technique in the M-Estimation Method to Estimate Parameters of Multiple Linear Regression: Simulation Study." Tikrit Journal of Administrative and Economic Sciences 19, no. 64, 1 (December 31, 2023): 712–25. http://dx.doi.org/10.25130/tjaes.19.64.1.38.
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The goal of this study is to develop a new technique for estimating the parameters of a multiple linear regression by using M-estimation based on scale estimator to handle the influence of outlier values. In order to get new estimators, the root mean square error (RMSE) criterion is used to check the efficiency between the new technique and the classical method. The research showed that the new technique (M-estimation based on scale estimator) yields more accurate parameter estimates than the traditional approach (OLS) in all simulated cases.
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Pinette, M. G., Y. Pan, S. G. Pinette, J. Blackstone, J. Garrett, and A. Cartin. "Estimation of fetal weight: mean value from multiple formulas." Journal of Ultrasound in Medicine 18, no. 12 (December 1999): 813–17. http://dx.doi.org/10.7863/jum.1999.18.12.813.
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Kim, Jae-Hee, and Sooy-Oung Cheon. "Multiple Change-Point Estimation of Air Pollution Mean Vectors." Korean Journal of Applied Statistics 22, no. 4 (August 31, 2009): 687–95. http://dx.doi.org/10.5351/kjas.2009.22.4.687.
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Rao, Zhushi, Qinzhong Shi, and Ichiro Hagiwara. "Optimal Estimation of Dynamic Loads for Multiple-Input System." Journal of Vibration and Acoustics 121, no. 3 (July 1, 1999): 397–401. http://dx.doi.org/10.1115/1.2893993.
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An optimal method is developed to estimate the dynamic loads for systems subjected to multiple inputs. The method focuses on minimizing the ensemble mean square error of the estimation. First, the inverse system analysis technique is employed to establish the error estimation equation. Then, by applying the noncausal Wiener filtering theory, the optimal estimator of dynamic loads is derived out. Numerical simulation work demonstrates that the method is of a good ability in suppressing the influence of measurement noises on estimation accuracy. Meanwhile, the simulating calculation of load estimation by a conventional method is also performed and the comparison of both results shows that the method proposed in this paper is rather effective and practicable for dynamic load estimation.
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Lee, Andrew Sanghyun, Yuandi Wu, Stephen Andrew Gadsden, and Mohammad AlShabi. "Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper." Sensors 24, no. 1 (December 31, 2023): 251. http://dx.doi.org/10.3390/s24010251.
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This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system’s mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper’s current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system’s behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations.
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Omar Gan, Sarimah, and Sabri Ahmad. "ESTIMATION OF TRADE BALANCE USING MULTIPLE LINEAR REGRESSION MODEL." Labuan Bulletin of International Business and Finance (LBIBF) 16 (November 30, 2018): 44–52. http://dx.doi.org/10.51200/lbibf.v16i.1642.
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This study aims to evaluate the performance of multiple linear regression in estimating trade balance, so that a regression model for estimating the trade balance can be developed based on the important variables that have been identified. The performance of four regression methods including enter, stepwise regression, backward deletion, and forward selection is measured by mean absolute error, standard deviation, and Pearson correlation at the validation stage. The study concludes that multiple linear regression model developed by stepwise method is the best model for the trade balance estimation. The model considers the following six significant variables: Exports of palm oil, imports of tubes, pipes, and fittings of iron or steel, exports of crude petroleum, imports of petroleum products, exports of plywood plain, and imports of rice. The regression model achieves a moderate value of model estimated accuracy (76.10%), mean absolute error (0.257), standard deviation (0.308), and linear correlation (0.851).
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Neubauer, Jirí, and Vítezslav Veselý. "Detection of multiple changes in mean by sparse parameter estimation." Nonlinear Analysis: Modelling and Control 18, no. 2 (April 25, 2013): 177–90. http://dx.doi.org/10.15388/na.18.2.14021.
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The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.
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Najlaa Ali Dhumad and Abbas Lafta Kneehr. "Comparison Of Some Estimation Methods For The Estimators Of Marshall Olkin Distribution With Simulation." Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa 2, no. 4 (August 19, 2024): 234–47. http://dx.doi.org/10.62383/bilangan.v2i4.204.
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The research comprised multiple simulated tests to determine the relationship between (sample size, distribution parameter value, estimation method, and pollution indivuduales). The experimental findings indicate that the estimator is influenced by sample size, the value of distribution parameter, estimation method, and pollution indivuduales. The results of the mean square error analysis indicate that (robust estimation method) produces the best results with the lowest mean square error, and the best estimation method was (191) of (243) simulation experiments. Additional statistical distributions with additional factors can be performed to demonstrate additional results.
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González-Coma, José P., Pedro Suárez-Casal, Paula M. Castro, and Luis Castedo. "FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems." Sensors 20, no. 3 (February 10, 2020): 930. http://dx.doi.org/10.3390/s20030930.
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A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.
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Pan, Yan, Li Zhang, Liyan Xu, and Fabing Duan. "DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification." Sensors 24, no. 14 (July 20, 2024): 4719. http://dx.doi.org/10.3390/s24144719.
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Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.
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Paizi, Koorosh, Hossein Parsaei, and Mohammad Mehdi Movahedi. "A MULTIPLE MODEL ALGORITHM FOR ESTIMATING MOTOR UNIT FIRING PATTERN STATISTICS." Biomedical Engineering: Applications, Basis and Communications 30, no. 06 (November 29, 2018): 1850047. http://dx.doi.org/10.4015/s1016237218500473.
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The firing patterns and recruitment behavior of motor units (MUs) during a muscle contraction can be used in diagnosing neuromuscular disorder, studying motor control, improving the performance of electromyography (EMG) signal decomposition, and assessing the validity of MU potential trains extracted by an EMG decomposition algorithm. However, MU firing patterns extracted via EMG decomposition might contain several missed or erroneous that can lead to misleading conclusion. In this paper, we presented a multiple model estimation system (MMES) for estimating the mean ([Formula: see text]) and standard deviation ([Formula: see text]) of inter-discharge intervals (IDIs) of a MU. The presented MMES aggregates an existing error-filtered estimation (EFE) algorithm and a multiple linear regression model to estimate both [Formula: see text] and [Formula: see text]. The MMES estimates these two parameters using 10 features extracted from given IDIs and initial estimation of [Formula: see text] and [Formula: see text] values provided by EFE algorithm. Evaluation results using both simulated and real IDIs revealed that MMES performed better than EFE algorithm in estimating both [Formula: see text] and [Formula: see text] values in terms of root mean square error (RMSE), estimating variance and the range of estimated values. The RMSE values for MMES in estimating [Formula: see text] for simulated and real IDIs, respectively, were [Formula: see text] and [Formula: see text] that are statistically lower than that of EFE algorithm with RMSE [Formula: see text] and [Formula: see text]. In estimating [Formula: see text] in simulated and real IDIs, RMSE values of MMES, respectively were [Formula: see text] and [Formula: see text] that was significantly lower than those values obtained for EFE [Formula: see text] (for simulated IDIs) and [Formula: see text] (for real IDIs). More importantly, MMES outperformed EFE algorithm for real train with right-skewed IDI distribution. Consequently, MMES is more accurate, reliable and consistent than EFE algorithm for estimating IDI mean and standard deviation.
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Swapna, Sonti. "Channel Estimation for MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 201–4. http://dx.doi.org/10.22214/ijraset.2022.39776.
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Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM
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Babino, Lucia, Andrea Rotnitzky, and James Robins. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes." Biometrics 75, no. 1 (July 13, 2018): 90–99. http://dx.doi.org/10.1111/biom.12924.
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Hamada, Yuki, Colleen R. Zumpf, John J. Quinn, and Maria Cristina Negri. "Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA." Energies 16, no. 21 (November 2, 2023): 7397. http://dx.doi.org/10.3390/en16217397.
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We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.
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Rejeb, Nessrine Ben, Ines Bousnina, Mohamed Bassem Ben Salah, and Abdelaziz Samet. "Mean angle of arrival, angular and Doppler spreads estimation in multiple‐input multiple‐output system." IET Signal Processing 9, no. 5 (July 2015): 395–402. http://dx.doi.org/10.1049/iet-spr.2014.0173.
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Rastogi, Krati, and Divya Lohani. "Edge Computing-Based Internet of Things Framework for Indoor Occupancy Estimation." International Journal of Ambient Computing and Intelligence 11, no. 4 (October 2020): 16–37. http://dx.doi.org/10.4018/ijaci.2020100102.
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Indoor occupancy estimation has become an important area of research in the recent past. Information about the number of people entering or leaving a building is useful in estimation of hourly sales, dynamic seat allocation, building climate control, etc. This work proposes a decentralized edge computing-based IoT framework in which the majority of the data analytics is performed on the edge, thus saving a lot of time and network bandwidth. For occupancy estimation, relative humidity and carbon dioxide concentration are used as inputs, and estimation models are developed using multiple linear regression, quantile regression, support vector regression, kernel ridge regression, and artificial neural networks. These estimations are compared using execution speed, power consumption, accuracy, root mean square error, and mean absolute percentage error.
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Memic, Emir, Simone Graeff, Kenneth J. Boote, Oliver Hensel, and Gerrit Hoogenboom. "Cultivar Coefficient Estimator for the Cropping System Model Based on Time-Series Data: A Case Study for Soybean." Transactions of the ASABE 64, no. 4 (2021): 1391–402. http://dx.doi.org/10.13031/trans.14432.
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HighlightsSoftware was developed for estimation of DSSAT CSM-CROPGRO-Soybean cultivar coefficients.Phenology-related coefficients were estimated based on observed phenological events.Growth-related cultivar coefficients were estimated based on time-series observations.Cultivar coefficients were optimized based on single- and multiple-experiment data sets.Abstract. The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most popular software solutions for predicting crop growth and yield while capturing the effects of management practices and interactions between the crop and the environment. Accurate estimation of the crop cultivar coefficients that govern in-season growth and development is critical for correct yield estimates. The manual cultivar coefficient estimation process is time-consuming and results in user-dependent, subjective optimums that are difficult to reproduce. Typically, end-of-season observations (point-based) are used for estimating dynamic in-season biomass accumulation rates. The objective of this study was to develop a time-series estimator (TSE) capable of using multiple in-season observations for estimating the coefficients that define in-season growth and biomass partitioning. Using the TSE, cultivar coefficients were estimated based on multiple in-season observations of leaf area index (LAI) and shoot, leaf, and grain dry matter weights. The cultivar coefficients were estimated from single- and multiple-treatment (seasons and locations) in-season observations. This was done for two cultivars for six management × environment combinations. Estimated multiple-treatment based cultivar coefficients were evaluated with an independent data set and compared to DSSAT standard (manual) coefficients and the cultivar coefficients estimated with the GLUE method. The average normalized root mean squared error (nRSME) for LAI and shoot, leaf, and grain weights was 26% lower for one cultivar and about the same for the other cultivar when compared to the DSSAT standard. Because GLUE uses end-of-season point-based cultivar coefficient estimation, the grain weight over time was underestimated in earlier phases and more accurate toward harvest. The TSE-estimated cultivar coefficients based on 346 in-season observations across multiple target variables and six experiments more accurately reflected in-season growth and grain weight without compromising final grain weight predictions. Keywords: . CROPGRO-Soybean, DSSAT, Genetic coefficients, Normalized root mean square error minimization, Time-seris observations.
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Liu, Huan Bin. "Study on Asymptotic Property of Additive-Accelerated Mean Regression Model." Advanced Engineering Forum 6-7 (September 2012): 49–53. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.49.
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Recurrent event data is a kind of important incomplete data existed in survival analysis, biological medicine research, reliability life test and other practical problems. This paper presents an additive-accelerated mean regression model for multiple type recurrent events data, and gives the estimation methods of unknown parameter and non-parameter function. Specially, the asymptotic properties of parameters estimation are proved.
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Mphekgwana, Peter M., Yehenew G. Kifle, and Chioneso S. Marange. "Pretest Estimation for the Common Mean of Several Normal Distributions: In Meta-Analysis Context." Axioms 13, no. 9 (September 22, 2024): 648. http://dx.doi.org/10.3390/axioms13090648.
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The estimation of unknown quantities from multiple independent yet non-homogeneous samples has garnered increasing attention in various fields over the past decade. This interest is evidenced by the wide range of applications discussed in recent literature. In this study, we propose a preliminary test estimator for the common mean (μ) with unknown and unequal variances. When there exists prior information regarding the population mean with consideration that μ might be equal to the reference value for the population mean, a hypothesis test can be conducted: H0:μ=μ0 versus H1:μ≠μ0. The initial sample is used to test H0, and if H0 is not rejected, we become more confident in using our prior information (after the test) to estimate μ. However, if H0 is rejected, the prior information is discarded. Our simulations indicate that the proposed preliminary test estimator significantly decreases the mean squared error (MSE) values compared to unbiased estimators such as the Garybill-Deal (GD) estimator, particularly when μ closely aligns with the hypothesized mean (μ0). Furthermore, our analysis indicates that the proposed test estimator outperforms the existing method, particularly in cases with minimal sample sizes. We advocate for its adoption to improve the accuracy of common mean estimation. Our findings suggest that through careful application to real meta-analyses, the proposed test estimator shows promising potential.
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Khan, Imran, Mohammad Zafar, Majid Ashraf, and Sunghwan Kim. "Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems." Electronics 7, no. 12 (December 3, 2018): 382. http://dx.doi.org/10.3390/electronics7120382.
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Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.
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Longoria-Gandara, O., R. Parra-Michel, M. Bazdresch, and A. G. Orozco-Lugo. "Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation." International Journal of Digital Multimedia Broadcasting 2008 (2008): 1–9. http://dx.doi.org/10.1155/2008/535269.
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This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.
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K, Oguagbaka, S., Okoli, O. C, and Aronu, C. O. "Ratio estimator for double sampling procedure with non-response: An empirical study." International Journal of Basic and Applied Science 12, no. 4 (April 7, 2024): 148–58. http://dx.doi.org/10.35335/ijobas.v12i4.281.
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This study proposes a ratio-type estimator for population mean estimation using auxiliary variables with double sampling in the presence of non-response. The study provides expressions for the constant, bias, and mean square errors (MSE) of the proposed estimator and compares it with ten existing estimators. The study employed the secondary source of data collection to evaluate the efficiency of the proposed and existing estimators by analyzing five natural populations from three different sources. The performance of ten (10) estimators was considered in this study. The findings suggest that the proposed estimator and the H estimator provide more accurate and precise estimates of the population mean using an auxiliary variable. Additionally, the study found significant differences amongst the mean values of the constant and bias for the different estimators. A Dunn Kruskal-Wallis multiple comparison tests with the Bonferroni method was performed to ascertain the pair of estimators that contributed to the significant difference observed. When estimating the population means using an auxiliary variable, the proposed estimator outperformed other existing estimators that were taken into consideration in the study
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Fu, Hongyu, Chufeng Wang, Guoxian Cui, Wei She, and Liang Zhao. "Ramie Yield Estimation Based on UAV RGB Images." Sensors 21, no. 2 (January 19, 2021): 669. http://dx.doi.org/10.3390/s21020669.
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Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations.
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Cheon, Sooyoung, and Wenxing Yu. "Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data." Korean Journal of Applied Statistics 25, no. 6 (December 31, 2012): 999–1008. http://dx.doi.org/10.5351/kjas.2012.25.6.999.
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Miller, M. I., A. Srivastava, and U. Grenander. "Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition." IEEE Transactions on Signal Processing 43, no. 11 (1995): 2678–90. http://dx.doi.org/10.1109/78.482117.
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魏, 秋月. "Research on the Estimation of Common Mean for Multiple Log-Normal Populations." Statistics and Application 07, no. 05 (2018): 516–20. http://dx.doi.org/10.12677/sa.2018.75060.
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Lu, Wei, Yongliang Wang, Xiaoqiao Wen, Shixin Peng, and Liang Zhong. "Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference." Electronics 8, no. 5 (April 28, 2019): 473. http://dx.doi.org/10.3390/electronics8050473.
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We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér–Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations.
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Barrera-Causil, Carlos Javier, and Juan Carlos Correa-Morales. "Elicitation of the Parameters of Multiple Linear Models." Revista Colombiana de Estadística 44, no. 1 (January 15, 2021): 159–70. http://dx.doi.org/10.15446/rce.v44n1.83525.
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Estimating the parameters of a multiple linear model is a common task in all areas of sciences. In order to obtain conjugate distributions, the Bayesian estimation of these parameters is usually carried out using noninformative priors. When informative priors are considered in the Bayesian estimation an important problem arises because techniques arerequired to extract information from experts and represent it in an informative prior distribution. Elicitation techniques can be used for suchpurpose even though they are more complex than the traditional methods. In this paper, we propose a technique to construct an informative prior distribution from expert knowledge using hypothetical samples. Our proposal involves building a mental picture of the population of responses at several specific points of the explanatory variables of a given model andindirectly eliciting the mean and the variance at each of these points. In addition, this proposal consists of two steps: the first step describes the elicitation process and the second step shows a simulation process to estimate the model parameters.
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Ayyildiz, Mustafa. "Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot." Sensor Review 39, no. 5 (September 16, 2019): 716–23. http://dx.doi.org/10.1108/sr-02-2019-0051.
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Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function. Findings In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R2 (97.5 per cent) and testing values R2 (99 per cent). The R2 for multiple regressions was obtained as 96.1 per cent. Originality/value The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.
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Yamaguchi, Nobuhiko, Hiroshi Okumura, Osamu Fukuda, Wen Liang Yeoh, and Munehiro Tanaka. "Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 2 (March 20, 2024): 352–60. http://dx.doi.org/10.20965/jaciii.2024.p0352.
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The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.
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Li, Shuang Zhi, Zhe Zhang, Xiao Min Mu, and Jian Kang Zhang. "Joint Multiple-Access Channel Effective Order and CIR Estimation Algorithm for Multi-User OFDM/SDMA System." Applied Mechanics and Materials 548-549 (April 2014): 1227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1227.
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In this paper, a novel joint estimation of channel effective order and channel impulse response (CIR) is presented for multiuser orthogonal frequency division multiplexing/space-division multiple-access (OFDM/SDMA) systems. By exploiting Akaike’s Information Criterion (AIC) as the fitness function to search the optimal order, the proposed scheme performs the channel effective order and CIR estimation in a parallel way based on differential evolution (DE) algorithm. Simulation results demonstrate that the proposed scheme is capable of attaining a better mean square error performance than the fixed channel order scheme, and improving the performance of time-domain maximum likelihood (ML) channel estimator.
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Aidara, Cherif Ahmat Tidiane. "Enhancing Multiple Frame Surveys: Improved Calibration and Efficient Bootstrap Techniques." European Journal of Statistics 4 (January 18, 2024): 1. http://dx.doi.org/10.28924/ada/stat.4.1.
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In recent years, multiple frame surveys have gained significant attention due to their applicability in capturing special or challenging-to-sample populations. This paper introduces two methodological advancements, the calibrated multiplicity estimator and without-replacement bootstrap techniques, in the field of multiple frame surveys. A comprehensive simulation study assesses their performance. The calibrated multiplicity estimator is demonstrated to outperform the multiplicity estimator, particularly in terms of mean squared error, with a ratio ranging from 0.6 to 0.8. Furthermore, the study shows that without-replacement bootstrap techniques perform favorably compared to their with-replacement counterparts. Future research directions include conducting more extensive simulations with real-world data and establishing the theoretical properties of the proposed estimator. This paper contributes to the growing body of knowledge on multiple frame surveys and their estimation methods.
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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (August 6, 2021): 5316. http://dx.doi.org/10.3390/s21165316.
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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.
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Wilson, D. K., Chris L. Pettit, and Vladimir E. Ostashev. "Bayesian estimation of mean transmission loss along multiple paths with randomly scattered signals." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1678. http://dx.doi.org/10.1121/1.5067469.
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Chen, Yuhan, Qingyun Yan, and Weimin Huang. "MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images." Remote Sensing 15, no. 23 (November 29, 2023): 5552. http://dx.doi.org/10.3390/rs15235552.
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The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (R2) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.
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Chang, Miao Miao, Jin He Zhou, and Ju Rong Wang. "Research of Improved Algorithm for MIMO Channel Estimation." Applied Mechanics and Materials 475-476 (December 2013): 893–99. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.893.
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We introduced an improved singular value decomposition (SVD) channel estimation algorithm for multiple-input multiple-output (MIMO) wireless communication system. The algorithm is supposed to solve the issue that the channel estimation result is not accurate when the training sequences have some 0 elements. The improvement is also applicable in the other channel estimation algorithms. We made some comparisons between the linear least squares (LS) and the linear minimum mean square error (LMMSE) channel estimation, the traditional singular value decomposition and the improved SVD algorithm to demonstrate the efficiency. Results show that the proposed improved SVD algorithm has better performance in mean square error (MSE) and bit error rate (BER) of channel estimation and the estimated values approach the actual channel state.
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ZATEROGLU, Mine Tulin. "Estimation of Cloudiness Data Based on Multiple Linear Regression Model." Karadeniz Fen Bilimleri Dergisi 13, no. 1 (March 15, 2023): 33–41. http://dx.doi.org/10.31466/kfbd.1150879.
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This study estimates cloudiness data using meteorological parameters which include climatic variables and air quality index. Daily average observed values of all meteorological parameters used in this study were transformed to monthly mean data for 1990-2015 period. The monthly mean values of cloudiness were estimated by using the other climatic elements and the value air quality index at urban area in Kayseri. Multiple Linear Regression model was built to determine the mathematical relationships for predicting cloudiness. It has been shown that meteorological parameters affect cloudiness the most in May and October, and the least in September and January. Additionally, according to the estimated models, air quality index value has effect on cloudiness data on January, July, October and November as statistically significant.
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Campelo, Felipe, and Elizabeth F. Wanner. "Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances." Journal of Heuristics 26, no. 6 (August 5, 2020): 851–83. http://dx.doi.org/10.1007/s10732-020-09454-w.
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Abstract This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm’s step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms.
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Lipovetsky, Stan. "Equation of Finite Change and Structural Analysis of Mean Value." Axioms 12, no. 10 (October 12, 2023): 962. http://dx.doi.org/10.3390/axioms12100962.
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This paper describes a problem of finding the contributions of multiple variables to a change in their function. Such a problem is well known in economics, for example, in the decomposition of a change in the mean price via the varying in time prices and volumes of multiple products. Commonly, it is considered by the tools of index analysis, the formulae of which present rather heuristic constructs. As shown in this work, the multivariate version of the Lagrange mean value theorem can be seen as an equation of the function’s finite change and solved with respect to an interior point whose value is used in the estimation of the contribution of the independent variables. Consideration is performed on the example of the weighted mean value function, which is the main characteristic of statistical estimation in various fields. The solution for this function can be obtained in the closed form, which helps in the analysis of results. Numerical examples include the cases of Simpson’s paradox, and practical applications are discussed.
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Li, Hui-Bin, Xiao-Rong Guan, Zhong Li, Kai-Fan Zou, and Long He. "Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression." Sensors 23, no. 10 (May 20, 2023): 4934. http://dx.doi.org/10.3390/s23104934.
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In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R2 of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer’s motion intentions in human–robot collaboration control.
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Garcia Guzman, Yuneisy E., and Michael Lunglmayr. "Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation." Signals 2, no. 2 (April 15, 2021): 189–200. http://dx.doi.org/10.3390/signals2020015.
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The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived and an algorithm based on sparse cyclic coordinate descent (SCCD) for estimating the frequency parameters is proposed. The algorithm adaptively reduces the size of the used frequency grid, which eases the computational burden. Simulation results revealed that the proposed algorithm achieves similar performance to the original formulation and the Root-multiple signal classification (MUSIC) algorithm in terms of the mean square error (MSE), with significantly less complexity.
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Longoria-Gandara, Omar, Ramon Parra-Michel, Roberto Carrasco-Alvarez, and Eduardo Romero-Aguirre. "Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3723862.
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This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.
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Asghar, Amber, Aamir Sanaullah, Muhammad Hanif, and Laila A. Al-Essa. "Enhancing Precision in Population Variance Vector Estimation: A Two-Phase Sampling Approach with Multi-Auxiliary Information." Sains Malaysiana 53, no. 7 (July 31, 2024): 1693–702. http://dx.doi.org/10.17576/jsm-2024-5307-16.
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To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector using multi-auxiliary variables is a less explored area in current literature. This paper addresses the estimation of vector of unknown population variances for multiple study variables by using an estimated vector of variances derived from multi-auxiliary information. This approach is particularly relevant when population variances for the multi-auxiliary variables are not known prior to the survey. The paper introduces a generalized variance and a vector of biases for the proposed multivariate estimator. Special cases of the proposed multivariate variance estimator are provided, accompanied by expressions for mean square errors. Theoretical mathematical conditions are discussed to guide the preference for the proposed estimator. Through the analysis of real-world application-based data, the applicability and efficiency of the proposed multivariate variance estimator are demonstrated, outperforming modified versions of multivariate variance estimators. Additionally, a simulation study validates the superior performance of the proposed estimator compared to its modified estimators.
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Hansen, Bruce E. "SHRINKAGE EFFICIENCY BOUNDS." Econometric Theory 31, no. 4 (October 2, 2014): 860–79. http://dx.doi.org/10.1017/s0266466614000693.
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This paper is an extension of Magnus (2002, Econometrics Journal 5, 225–236) to multiple dimensions. We consider estimation of a multivariate normal mean under sum of squared error loss. We construct the efficiency bound (the lowest achievable risk) for minimax shrinkage estimation in the class of minimax orthogonally invariate estimators satisfying the sufficient conditions of Efron and Morris (1976, Annals of Statistics 4, 11–21). This allows us to compare the regret of existing orthogonally invariate shrinkage estimators. We also construct a new shrinkage estimator which achieves substantially lower maximum regret than existing estimators.
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Zahid, Faisal M., and Christian Heumann. "Multiple imputation with sequential penalized regression." Statistical Methods in Medical Research 28, no. 5 (February 16, 2018): 1311–27. http://dx.doi.org/10.1177/0962280218755574.
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Missing data is a common issue that can cause problems in estimation and inference in biomedical, epidemiological and social research. Multiple imputation is an increasingly popular approach for handling missing data. In case of a large number of covariates with missing data, existing multiple imputation software packages may not work properly and often produce errors. We propose a multiple imputation algorithm called mispr based on sequential penalized regression models. Each variable with missing values is assumed to have a different distributional form and is imputed with its own imputation model using the ridge penalty. In the case of a large number of predictors with respect to the sample size, the use of a quadratic penalty guarantees unique estimates for the parameters and leads to better predictions than the usual Maximum Likelihood Estimation (MLE), with a good compromise between bias and variance. As a result, the proposed algorithm performs well and provides imputed values that are better even for a large number of covariates with small samples. The results are compared with the existing software packages mice, VIM and Amelia in simulation studies. The missing at random mechanism was the main assumption in the simulation study. The imputation performance of the proposed algorithm is evaluated with mean squared imputation error and mean absolute imputation error. The mean squared error ([Formula: see text]), parameter estimates with their standard errors and confidence intervals are also computed to compare the performance in the regression context. The proposed algorithm is observed to be a good competitor to the existing algorithms, with smaller mean squared imputation error, mean absolute imputation error and mean squared error. The algorithm’s performance becomes considerably better than that of the existing algorithms with increasing number of covariates, especially when the number of predictors is close to or even greater than the sample size. Two real-life datasets are also used to examine the performance of the proposed algorithm using simulations.
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Fang, Cheng, Zhenlong Wu, Haikun Zheng, Jikang Yang, Chuang Ma, and Tiemin Zhang. "MCP: Multi-Chicken Pose Estimation Based on Transfer Learning." Animals 14, no. 12 (June 12, 2024): 1774. http://dx.doi.org/10.3390/ani14121774.
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Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the “multi-chicken pose” (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis
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Jeon, Heegyun, Sungmin Aum, Hyungbo Shim, and Yongsoon Eun. "Resilient State Estimation for Control Systems Using Multiple Observers and Median Operation." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3750264.
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This paper addresses the problem of state estimation for linear dynamic systems that is resilient against malicious attacks on sensors. By “resiliency” we mean the capability of correctly estimating the state despite external attacks. We propose a state estimation with a bank of observers combined through median operations and show that the proposed method is resilient in the sense that estimated states asymptotically converge to the true state despite attacks on sensors. In addition, the effect of sensor noise and process disturbance is also considered. For bounded sensor noise and process disturbance, the proposed method eliminates the effect of attack and achieves state estimation error within a bound proportional to those of sensor noise and disturbance. While existing methods are computationally heavy because online solution of nonconvex optimization is needed, the proposed approach is computationally efficient by using median operation in the place of the optimization. It should be pointed out that the proposed method requires the system states being observable with every sensor, which is not a necessary condition for the existing methods. From resilient system design point of view, however, this fact may not be critical because sensors can be chosen for resiliency in the design stage. The gained computational efficiency helps real-time implementation in practice.
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Kim, Ki-Hun, and Kwang-Jae Kim. "Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data." JMIR Medical Informatics 8, no. 12 (December 17, 2020): e20597. http://dx.doi.org/10.2196/20597.
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Background A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is a lack of research in this area. Objective This study aims to identify a suitable missing-data handling method for LWI estimation with panel data. Methods Listwise deletion, mean imputation, expectation maximization–based multiple imputation, predictive-mean matching–based multiple imputation, k-nearest neighbors–based imputation, and low-rank approximation–based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1% to 80%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set. Results Methods performed differently depending on the proportion of missing data. For 1% to 30% proportions, low-rank approximation–based imputation, predictive-mean matching–based multiple imputation, and expectation maximization–based multiple imputation were superior. For 31% to 60% proportions, low-rank approximation–based imputation and predictive-mean matching–based multiple imputation performed best. For over 60% proportions, only low-rank approximation–based imputation performed acceptably. Conclusions Low-rank approximation–based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation–based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data.