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Articoli di riviste sul tema "Multiple mean estimation"

Autore: Grafiati

Pubblicato: 26 ottobre 2024

Ultima modifica: 31 luglio 2025

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1

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 (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

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2

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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3

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 (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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4

Rao, Zhushi, Qinzhong Shi, and Ichiro Hagiwara. "Optimal Estimation of Dynamic Loads for Multiple-Input System." Journal of Vibration and Acoustics 121, no. 3 (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 esti

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5

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 (1999): 813–17. http://dx.doi.org/10.7863/jum.1999.18.12.813.

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6

Kim, Jae-Hee, and Sooy-Oung Cheon. "Multiple Change-Point Estimation of Air Pollution Mean Vectors." Korean Journal of Applied Statistics 22, no. 4 (2009): 687–95. http://dx.doi.org/10.5351/kjas.2009.22.4.687.

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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 (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 o

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8

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 estima

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9

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 (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 distribut

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10

Neubauer, Jirí, and Vítezslav Veselý. "Detection of multiple changes in mean by sparse parameter estimation." Nonlinear Analysis: Modelling and Control 18, no. 2 (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 t

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11

Roji, Y., Prasad Avutha Shiva, Dakoori Arun, and Dandu Binith. "Channel Estimation of ZF and MMSE in MIMO System." Channel Estimation of ZF and MMSE in MIMO System 8, no. 11 (2023): 7. https://doi.org/10.5281/zenodo.10212388.

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Enhancing communication efficiency and dependability in multiple input/multiple output systems (MIMO) is largely dependent on channel estimation. Two popular methods for calculating mean square error (MMSE) and zero forcing (ZF) are obtaining precise channel estimation. By creating an inverted channel matrix, the Zero Forcing method seeks to neutralize interference and essentially eliminate the influence of inter-symbol interference. Though it may be susceptible to errors and noise, this approach is computationally efficient. On the other hand, the aim of lowest mean square error channel estim

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12

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 (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

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13

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 (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 pr

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14

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 (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 s

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Swapna, Sonti. "Channel Estimation for MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (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 Me

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16

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 (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 estimate

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17

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 (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

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18

Babino, Lucia, Andrea Rotnitzky, and James Robins. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes." Biometrics 75, no. 1 (2018): 90–99. http://dx.doi.org/10.1111/biom.12924.

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19

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

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20

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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21

Swapnaja, Deshpande, Aggarwal Mona, Sabherwal Pooja, and Ahuja Swaran. "Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks." Indian Journal of Science and Technology 17, no. 5 (2024): 465–77. https://doi.org/10.17485/IJST/v17i5.2961.

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Abstract <strong>Objectives:</strong> The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). <strong>Methods:</strong> The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFM

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22

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 (2015): 395–402. http://dx.doi.org/10.1049/iet-spr.2014.0173.

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23

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 (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 t

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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 (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

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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 MRS

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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 (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

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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 (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

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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 (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 propagat

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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 (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

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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 (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 techn

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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 (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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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 (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 pla

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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 fi

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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (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 constru

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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 stud

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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 d

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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 (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 un

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Yang, Shuo, Yong Li, Lizhe Liu, Jing Xia, Bin Wang, and Xingjian Li. "Channel Estimation for Massive MIMO Systems via Polarized Self-Attention-Aided Channel Estimation Neural Network." Entropy 27, no. 3 (2025): 220. https://doi.org/10.3390/e27030220.

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Research on deep learning (DL)-based channel estimation for massive multiple-input multiple-output (MIMO) communication systems has attracted considerable interest in recent years. In this paper, we propose a DL-assisted channel estimation algorithm that transforms the original channel estimation problem into an image denoising problem, contrasting it with traditional experience-based channel estimation methods. We establish a new polarized self-attention-aided channel estimation neural network (PACE-Net) to achieve efficient channel estimation. This approach addresses the limitations of the c

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Eze, Francis Chkwuemeka, and Victor Gozie Chukwunenye. "Comparing Methods of Estimating Missing Values in One Way Analysis of Variance." International Journal of Trend in Scientific Research and Development 3, no. 2 (2019): 994–1000. https://doi.org/10.31142/ijtsrd18599.

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It is obvious that the treatment of missing data has been an issue in statistics for some time now, and hence has started gaining the attention of researchers. This paper established the various methods usable in estimating missing values, determined which of the methods is the best in estimating missing values in one-way analysis of variance ANOVA , determined at which percentage level of Missingness is the method best and verified the effect of missing values on the statistical power and non-centrality parameters in one-way ANOVA. The methods examined are Pairwise Deletion PD , Mean Substitu

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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 (2018): 1678. http://dx.doi.org/10.1121/1.5067469.

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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 (2023): 4934. http://dx.doi.org/10.3390/s23104934.

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Abstract (sommario):

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 sup

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Lipovetsky, Stan. "Equation of Finite Change and Structural Analysis of Mean Value." Axioms 12, no. 10 (2023): 962. http://dx.doi.org/10.3390/axioms12100962.

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Abstract (sommario):

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 valu

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ZATEROGLU, Mine Tulin. "Estimation of Cloudiness Data Based on Multiple Linear Regression Model." Karadeniz Fen Bilimleri Dergisi 13, no. 1 (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

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Hansen, Bruce E. "SHRINKAGE EFFICIENCY BOUNDS." Econometric Theory 31, no. 4 (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 substantia

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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 (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. Th

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Rahmaddeni, Rahmaddeni, M. Teguh Wicaksono, Denok Wulandari, Agustriono Agustriono, and Sang Adji Ibrahim. "Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 1 (2024): 85–94. https://doi.org/10.30812/matrik.v24i1.4280.

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Abstract (sommario):

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a st

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Garcia Guzman, Yuneisy E., and Michael Lunglmayr. "Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation." Signals 2, no. 2 (2021): 189–200. http://dx.doi.org/10.3390/signals2020015.

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Abstract (sommario):

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

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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 cooper

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