Relevant bibliographies by topics / ML estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ML estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "ML estimation"

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Goel, Dr Shalini. "AQI ML Estimation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48125.

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Abstract— This study applies machine learning to predict the Air Quality Index (AQI) in Delhi, using data from 2015–2022 sourced from the Central Pollution Control Board. It considers key pollutants (PM2.5, PM10, NO₂, SO₂, CO, O₃) and meteorological factors. PM2.5 and vehicular emissions were identified as major AQI contributors. The findings support real-time AQI forecasting and align with UN SDGs 3 and 11, promoting public health and sustainable urban living through data-driven environmental strategies. Keywords— Air Quality Index, Machine Learning, Delhi Pollution, Predictive Modeling, Sust
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M., Alagurajan, and Vijayakumaran C. "ML Methods for Crop Yield Prediction and Estimation: An Exploration." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3506–8. https://doi.org/10.35940/ijeat.C5775.029320.

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Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.
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Williams, Brett, and Mal Boyle. "Estimation of External Blood Loss by Paramedics: Is There Any Point?" Prehospital and Disaster Medicine 22, no. 6 (2007): 502–6. http://dx.doi.org/10.1017/s1049023x0000532x.

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AbstractObjectives:There are many patient assessment challenges in the prehospital setting, especially the estimation of external blood loss. Previous studies of experienced paramedics have demonstrated that external blood loss estimation is highly inaccurate. The objective of this study was to determine if undergraduate paramedic students could accurately estimate external blood loss on four surfaces commonly found in the prehospital environment.Methods:This prospective, observational, blinded study used a convenience sample of undergraduate students studying at Monash University during 2006.
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Samar, Mahvish, Xinzhong Zhu, and Huiying Xu. "Conditioning Theory for ML-Weighted Pseudoinverse and ML-Weighted Least Squares Problem." Axioms 13, no. 6 (2024): 345. http://dx.doi.org/10.3390/axioms13060345.

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The conditioning theory of the ML-weighted least squares and ML-weighted pseudoinverse problems is explored in this article. We begin by introducing three types of condition numbers for the ML-weighted pseudoinverse problem: normwise, mixed, and componentwise, along with their explicit expressions. Utilizing the derivative of the ML-weighted pseudoinverse problem, we then provide explicit condition number expressions for the solution of the ML-weighted least squares problem. To ensure reliable estimation of these condition numbers, we employ the small-sample statistical condition estimation me
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Stoica, Petre, and Tomas Sundin. "Exact ML Estimation of Spectroscopic Parameters." Journal of Magnetic Resonance 145, no. 1 (2000): 108–14. http://dx.doi.org/10.1006/jmre.2000.2077.

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Zheng, Yu, Lutao Liu, and Xudong Yang. "SPICE-ML Algorithm for Direction-of-Arrival Estimation." Sensors 20, no. 1 (2019): 119. http://dx.doi.org/10.3390/s20010119.

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Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal. However, the signal power estimated by sparse iterative covariance-based estimation approach is inaccurate, and the estimation performance is limited to direction grid. To solve the problem above, an algorithm combing the sparse iterative covariance-based estimation approach and maximum likelihood estimation is proposed. The signal power estimated by sparse iterative covariance-based estimation approa
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Woltmann, Lucas, Claudio Hartmann, Dirk Habich, and Wolfgang Lehner. "Aggregate-based Training Phase for ML-based Cardinality Estimation." Datenbank-Spektrum 22, no. 1 (2022): 45–57. http://dx.doi.org/10.1007/s13222-021-00400-z.

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AbstractCardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to
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Zulkifli, Raudhah, Nazim Aimran, Sayang Mohd Deni, and Fatin Najihah Badarisam. "A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling." International Journal of Data and Network Science 6, no. 2 (2022): 391–400. http://dx.doi.org/10.5267/j.ijdns.2021.12.015.

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Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equat
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Liu, Yu-Sun, Shingchern You, and Yu-Chun Lai. "Machine Learning-Based Channel Estimation Techniques for ATSC 3.0." Information 15, no. 6 (2024): 350. http://dx.doi.org/10.3390/info15060350.

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Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances wit
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Bashir, Rab Nawaz. "Internet of Things (IoT) and Machine Learning (ML) Assisted Reference Evapotranspiration (ETO) Estimations." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 19, no. 2 (2021): 80–90. http://dx.doi.org/10.52584/qrj.1902.13.

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Reference Evapotranspiration (ETo) is the amount of irrigation water required by a model crop to grow at its optimal level. ETo determination is a complex process that requires complicated calculations with many variables involved. There is a need to determine the ETo from available environmental conditions. Internet of Things (IoT) and Machine Learning (ML) based ETo estimation is proposed. IoT-assisted directly captured temperature data from the crop field is used to estimate the ETo. The estimated ETo can be used in many Precisions Agriculture (PA) applications especially in Precision Irrig
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Dissertations / Theses on the topic "ML estimation"

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Liu, Kejing, and Michael Rice. "Data Aided ML Symbol Timing Estimation for MIMO Systems Using a Polyphase Filterbank." International Foundation for Telemetering, 2003. http://hdl.handle.net/10150/605379.

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International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>This paper derives the maximum likelihood (ML) symbol timing estimator for a frequency non-selective multiple-input, multiple output (MIMO) channel assuming linear modulation and known data and known channel gains. The non-frequency selective fading assumption means the multipath delay spread is small so that the symbol time delay is the same across all receive antennas. Both a closed-loop and an approximate open-loop architecture are demonstrated. The open-lo
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Gomes, Marco Aurelio Cazarotto 1984. "Filtragem otima para melhorar o desempenho de estimadores DOA-ML." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261946.

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Orientador: Amauri Lopes<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-14T21:35:53Z (GMT). No. of bitstreams: 1 Gomes_MarcoAurelioCazarotto_M.pdf: 1012758 bytes, checksum: 0c2ca6c09e4123b277735dca0f50a107 (MD5) Previous issue date: 2009<br>Resumo: Abordamos o problema de estimação de direção de chegada (DOA) de ondas planas usando um arranjo de sensores. Na literatura encontramos diversos estimadores para DOA, porém estamos considerando apenas os estimadores de Máxima Verossimilhança (ML)
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Avan, Muhammet. "Joint Frequency Offset And Channel Estimation." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610149/index.pdf.

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In this thesis study, joint frequency offset and channel estimation methods for single-input single-output (SISO) systems are examined. The performance of maximum likelihood estimate of the parameters are studied for different training sequences. Conventionally training sequences are designed solely for the channel estimation purpose. We present a numerical comparison of different training sequences for the joint estimation problem. The performance comparisons are made in terms of mean square estimation error (MSE) versus SNR and MSE versus the total training energy metrics. A novel estimation
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Mollén, Katarina. "Water Depth Estimation Using Ultrasound Pulses for Handheld Diving Equipment." Thesis, Linköpings universitet, Institutionen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117061.

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This thesis studies the design and implementation of an ultra-sonic water depth sounder. The depth sounder is implemented in a hand-held smart console used by divers. Since the idea of echo sounding is to measure the flight time between transmitting the signal and receiving the echo, the main challenge of this task is to find a time-of-flight (ToF) estimation for a signal in noise. It should be suitable for this specific application and robust when implemented in the device. The thesis contains an investigation of suitable ToF methods. More detailed evaluations of the matched filter, also know
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Sazak, Hakan Savas. "Estimation And Hypothesis Testing In Stochastic Regression." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/3/724294/index.pdf.

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Regression analysis is very popular among researchers in various fields but almost all the researchers use the classical methods which assume that X is nonstochastic and the error is normally distributed. However, in real life problems, X is generally stochastic and error can be nonnormal. Maximum likelihood (ML) estimation technique which is known to have optimal features, is very problematic in situations when the distribution of X (marginal part) or error (conditional part) is nonnormal. Modified maximum likelihood (MML) technique which is asymptotically giving the estimators equivalent to
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Olofsson, Ivar. "Enhancements in LTE OTDOA Positioning for Multipath Environments." Thesis, Linköpings universitet, Kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131821.

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By using existing radio network infrastructure, a user can be positioned even where GPS and other positioning technologies lack coverage. The LTE Positioning Protocol (LPP) supports user Reference Signal Time Difference (RSTD) reports based on the Time of Arrival (TOA) for a Positioning Reference Signal (PRS). In the current reporting format, only one RSTD for each base station is considered, but for indoor environments this is easily biased due to fading and multipath issues, resulting in a Non-Line of Sight (NLOS) bias. With a rich User Equipment (UE) feedback that can represent the multipat
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Mohammad, Maruf H. "Blind Acquisition of Short Burst with Per-Survivor Processing (PSP)." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/46193.

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This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the pe
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Fares, Fares. "Traitement des signaux Argos 4." Thesis, Toulouse, INPT, 2011. http://www.theses.fr/2011INPT0024/document.

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Cette thèse est dédié à l’étude de la problématique des interférences multi utilisateurs dans le système Argos et à la proposition des diverses techniques pour réduire les effets de ces interférences. Le système Argos est un système mondial de localisation et de collecte de données géo positionnées par satellite. Il permet à l’échelle mondiale de collecter et de traiter les données émises par des émetteurs installés sur la surface de terre. Ces émetteurs sont connus sous le nom de balises. Ces balises sont installées sur des voiliers, des stations météo, des bouées, ainsi que sur quelques anim
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Bacharach, Lucien. "Caractérisation des limites fondamentales de l'erreur quadratique moyenne pour l'estimation de signaux comportant des points de rupture." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS322/document.

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Cette thèse porte sur l'étude des performances d'estimateurs en traitement du signal, et s'attache en particulier à étudier les bornes inférieures de l'erreur quadratique moyenne (EQM) pour l'estimation de points de rupture, afin de caractériser le comportement d'estimateurs, tels que celui du maximum de vraisemblance (dans le contexte fréquentiste), mais surtout du maximum a posteriori ou de la moyenne conditionnelle (dans le contexte bayésien). La difficulté majeure provient du fait que, pour un signal échantillonné, les paramètres d'intérêt (à savoir les points de rupture) appartiennent à u
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Santos, Helton Saulo Bezerra dos. "Essays on Birnbaum-Saunders models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/87375.

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Nessa tese apresentamos três diferentes aplicações dos modelos Birnbaum-Saunders. No capítulo 2 introduzimos um novo método por função-núcleo não-paramétrico para a estimação de densidades assimétricas, baseado nas distribuições Birnbaum-Saunders generalizadas assimétricas. Funções-núcleo baseadas nessas distribuições têm a vantagem de fornecer flexibilidade nos níveis de assimetria e curtose. Em adição, os estimadores da densidade por função-núcleo Birnbaum-Saunders gene-ralizadas assimétricas são livres de viés na fronteira e alcançam a taxa ótima de convergência para o erro quadrático integ
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Books on the topic "ML estimation"

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Montier, James. Cointegration and VARS: Johansen's ML procedure : theory, estimation, identification and inference : a practitionersguide. University of Portsmouth, Dept. of Economics, 1994.

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Cheng, Russell. Standard Asymptotic Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0003.

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This book relies on maximum likelihood (ML) estimation of parameters. Asymptotic theory assumes regularity conditions hold when the ML estimator is consistent. Typically an additional third derivative condition is assumed to ensure that the ML estimator is also asymptotically normally distributed. Standard asymptotic results that then hold are summarized in this chapter; for example, the asymptotic variance of the ML estimator is then given by the Fisher information formula, and the log-likelihood ratio, the Wald and the score statistics for testing the statistical significance of parameter es
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Speeckaert, Marijn, and Jopis Delanghe. Assessment of renal function. Edited by Christopher G. Winearls. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0007.

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Glomerular filtration rate (GFR) can be measured as the clearance of exogenous or endogenous filtration markers. Practical formulas permit estimation of creatinine clearance or GFR without timed urine collections in many stable patients with CKD. Standardization of serum creatinine is important for all of these estimation methods and implementing traceability of the assays to the new global SRM 967 standard has led to changes in clinical decision-making criteria. Calibration to an IDMS reference produces a lowering of serum creatinine values by 10–30% for most methods. Serum creatinine concent
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Cheng, Russell. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0001.

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This chapter provides an overview of the book. The book investigates non-standard parametric, mainly continuous univariate estimation problems. The basic difference between standard and non-standard problems is explained in this chapter. The book considers different non-standard problems that can arise. Though some of the problems are advanced, a strong emphasis is placed on providing statistical methods to analyse them that are simple to understand and implement. Maximum likelihood (ML) estimation is the main method used to estimate parameters when fitting parametric models. This chapter outl
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Book chapters on the topic "ML estimation"

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Winkler, Gerhard. "Spacial ML Estimation." In Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-97522-6_15.

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Palladini, A., N. Testoni, L. De Marchi, and N. Speciale. "Ml Estimation for Acoustical Image Deblurring." In Acoustical Imaging. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8823-0_58.

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Schuster, Gerhard. "ML Estimation from Binomial Data with Misclassifications." In Econometrics in Theory and Practice. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-47027-1_5.

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Ollila, E., and V. Koivunen. "Robust ML-estimation of the Transmitter Location." In Theory and Applications of Recent Robust Methods. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_22.

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Chaudhuri, Subhasis, and A. N. Rajagopalan. "ML Estimation of Depth and Optimal Camera Settings." In Depth From Defocus: A Real Aperture Imaging Approach. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1490-8_6.

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Mariel, Petr, Danny Campbell, Erlend Dancke Sandorf, Jürgen Meyerhoff, Ainhoa Vega-Bayo, and Rebecca Blevins. "Maximum Likelihood and Related Issues." In The Economics of Non-Market Goods and Resources. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89338-4_8.

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Abstract This chapter explores Maximum Likelihood (ML) estimation, a statistical method used to estimate parameters of a given probability distribution. We begin with an introduction to the fundamental components of ML estimation, including the likelihood function, the density function, and the process of identifying parameter values that maximise the likelihood of the observed data. This chapter also covers numerical optimisation methods, both gradient-based and non-gradient, for situations where analytical solutions are impractical. We address sample variation in statistical estimation, high
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Aliferis, Constantin, and Gyorgy Simon. "Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI." In Health Informatics. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39355-6_10.

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AbstractAvoiding over and under fitted analyses (OF, UF) and models is critical for ensuring as high generalization performance as possible and is of profound importance for the success of ML/AI modeling. In modern ML/AI practice OF/UF are typically interacting with error estimator procedures and model selection, as well as with sampling and reporting biases and thus need be considered together in context. The more general situations of over confidence (OC) about models and/or under-performing (UP) models can occur in many subtle and not so subtle ways especially in the presence of high-dimens
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Griffith, Daniel A. "Quick but not so Dirty ML Estimation of Spatial Autoregressive Models." In Advances in Spatial Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00627-2_9.

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Goodwin-Allcock, Tobias, Jason McEwen, Robert Gray, Parashkev Nachev, and Hui Zhang. "How Can Spherical CNNs Benefit ML-Based Diffusion MRI Parameter Estimation?" In Computational Diffusion MRI. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21206-2_9.

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Kushwaha, Priyanka, Muskan Sharma, Pragati Kumari, and Richa Yadav. "Multi-sensor Data Fusion for Early Fire Estimation Using ML Techniques." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7216-6_22.

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Conference papers on the topic "ML estimation"

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Devigili, Mariano, Emilio Riccardi, Antonino Nespola, et al. "ML-Based Modeling of EDFA Pluggable Modules for OSNR Estimation." In 2025 International Conference on Optical Network Design and Modeling (ONDM). IEEE, 2025. https://doi.org/10.23919/ondm65745.2025.11029334.

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R, Aravindharaj, Athiniraj K, and T. V. Narmadha. "Optimised State of Charge Estimation for Battery Using ML Technique." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031976.

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Gao, Chenyu, and Ziping Zhao. "Beyond Jensen’s Inequality: Speeding Up ML Estimation of Generalized Hyperbolic Distributions." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888150.

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Tiwaskar, Shweta, Yash Gosavi, Swapnil Mohite, Sahil Lokhande, and Dhruv Mirajkar. "A Data-Driven Analysis of Crop Yield Estimation Using ML Methods." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11071096.

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Lollmann, Heinrich W., Andreas Brendel, and Walter Kellermann. "Efficient ML-Estimator for Blind Reverberation Time Estimation." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553001.

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Hawks, Ben, Dennis Plotnikov, Karla Tame-Narvaez, et al. "wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation." In wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation. US DOE, 2024. http://dx.doi.org/10.2172/2474968.

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Hawks, Ben, Dennis Plotnikov, Karla Tame-Narvaez, et al. "wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation." In wa-hls4ml: A benchmark and dataset for ML accelerator resource estimation. US DOE, 2024. http://dx.doi.org/10.2172/2474974.

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Vazquez-Padin, David, and Pedro Comesana. "ML estimation of the resampling factor." In 2012 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2012. http://dx.doi.org/10.1109/wifs.2012.6412650.

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Agee, B. G., and R. A. Calabretta. "ARMA-like and ML-like copy/DF approaches for signal-specific emitter location." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205561.

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Pourmottaghi, Abdollah, Mohammad Reza Taban, Yaser Norouzi, and Mohammad Taghi Sadeghi. "A robust CFAR detection with ML estimation." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4720885.

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Reports on the topic "ML estimation"

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Chaffa, Lucien, Martin Trépanier, and Thierry Warin. Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade. CIRANO, 2025. https://doi.org/10.54932/bfky4995.

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This study investigates the potential of machine learning (ML) methods to enhance the estimation of the gravity model, a cornerstone of international trade analysis that explains trade flows based on economic size and distance. Traditionally estimated using methods such as the Poisson Pseudo Maximum Likelihood (PPML) approach, gravity models often struggle to fully capture nonlinear relationships and intricate interactions among variables. Leveraging data from Canada and the US, one of the largest bilateral trading relationships in the world, this paper conducts a comparative analysis of tradi
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Mohanty, Subhasish, and Joseph Listwan. A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1688432.

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