Sparse Aperture Inverse Synthetic Aperture Radar Imaging for Maneuvering Targets With Migration Through Resolution Cells Correction by Jiaxing Yang, Yong Wang

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Modified Weights-of-Evidence Modeling with Example of Missing Geochemical Data by Daojun Zhang, Frits Agterberg

“…WofE allows construction of input layers that have missing data as a separate category in addition to known presence-absence type input, while logistic regression as such is not capable of handling missing data. …”
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Scattered Data Processing Approach Based on Optical Facial Motion Capture by Qiang Zhang, Xiaoying Liang, Xiaopeng Wei

“…Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. …”
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A Chain Ratio Exponential-Type Compromised Imputation for Mean Estimation: Case Study on Ozone Pollution in Saraburi, Thailand by Kanisa Chodjuntug, Nuanpan Lawson

“…We need to deal with missing data in a proper way before analysis using standard statistical techniques. …”
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Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable by Sara Javadi, Abbas Bahrampour, Mohammad Mehdi Saber, Behshid Garrusi, Mohammad Reza Baneshi

“…Multiple imputation by chained equations (MICE) is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. …”
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K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data by Ayesha Sania, Ayesha Sania, Nicolò Pini, Nicolò Pini, Morgan E. Nelson, Michael M. Myers, Michael M. Myers, Lauren C. Shuffrey, Maristella Lucchini, Maristella Lucchini, Amy J. Elliott, Amy J. Elliott, Hein J. Odendaal, William P. Fifer, William P. Fifer

“…Since participants with no missing days were not comparable to those with missing data, segments of non-missing data from all participants were included as a reference. …”
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Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control by Tianyu Shi, Francois-Xavier Devailly, Denis Larocque, Laurent Charlin

“…First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. …”
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Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…Sonic log data are particularly prone to such gaps, as they are newer and less common in older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. …”
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Improving the selection of object-analogues of oil and gas fields in designing reservoir engineering by Alexander V. Karsakov, Pavel N. Zyatikov, Kirill V. Sinebriukhov, Irina V. Sharf

“…The commissioning of a large number of new fields with limited amount of initial geological and physical data. To fill in the missing data, the selection of object-analogs is carried out. …”
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Preprocessing Approach for Power Transformer Maintenance Data Mining Based on k-Nearest Neighbor Completion and Principal Component Analysis by Moïse Manyol, Samuel Eke, Alphonse J. M. Massoma, Alain Biboum, Ruben Mouangue

“…In reality, many databases are characterized by attributes with outliers, redundant, and even more missing values. Missing data and outliers are ubiquitous in our databases, and imputation techniques will help us mitigate their influence. …”
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Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations. by Cécile Triay, Alice Boizet, Christopher Fragoso, Anestis Gkanogiannis, Jean-François Rami, Mathias Lorieux

“…The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. …”
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THE METHOD OF FORMING CAE MODELS ON THE EXAMPLE OF DESIGN AND TECHNOLOGICAL ELABORATION OF THE PLUNGER OF FORCED HYDROMACHINE by A. V. Puzanov

“…The paper proposed a method of forming CAE models from CAD models, taking into account simplification models (with the exception of nonfunctional elements, the use of symmetry, etc.), add the missing data (including the use of the properties of materials differ from the CAD model), the possibility of a multidisciplinary analysis in one or more software systems. …”
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Estimating Potential Evapotranspiration by Missing Temperature Data Reconstruction by Eladio Delgadillo-Ruiz, Eusebio Jr. Ventura-Ramos, Julián González Trinidad, Hugo Enrique Júnez-Ferreira, Carlos Francisco Bautista-Capetillo, Olivia Delgadillo-Ruiz

“…The purpose of this study was as follows: first, to apply a missing data reconstruction scheme in weather stations of the Rio Queretaro basin; second, to reduce the generated uncertainty of temperature data: mean, minimum, and maximum values in the evapotranspiration calculation which has a paramount importance in the manner of obtaining the water balance at any hydrological basin. …”
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Optimization of multiple sampling for solving network boundary specification problem by Ruochen Zhang

“…Abstract Missing data caused by boundary specification has a detrimental effect on the analysis of network structures, and designing optimal sampling methods is crucial for conducting network investigations. …”
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Optimal Imputation Methods under Stratified Ranked Set Sampling by Shashi Bhushan, Anoop Kumar

“…This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. …”
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Grading Prediction of Enterprise Financial Crisis Based on Nonlinear Programming Evaluation: A Case Study of Chinese Transportation Industry by Zhi-yuan Li

“…The proposed model can deal with the case of missing data, and has the good isotonic property and profound theoretical background. …”
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A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation by Zhipeng Gao, Weijing Cheng, Xuesong Qiu, Luoming Meng

“…To address this problem, Temporal and Spatial Correlation Algorithm (TSCA) is proposed to estimate missing data as accurately as possible in this paper. …”
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A Data-Driven Approach to Estimate Incident-Induced Delays Using Incomplete Probe Vehicle Data: Application to Safety Service Patrol Program Evaluation by Minsoo Oh, Jing Dong-O’Brien

“…This paper presents a data-driven approach to estimate incident-induced delays (IIDs) using probe vehicle data while accounting for missing data. The proposed approach is applied to evaluate the effectiveness of a safety service patrol (SSP) program. …”
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A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery by Xintong Zou, Hui Jin

“…To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. …”
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An Algorithm Using DBSCAN to Solve the Velocity Dealiasing Problem by Wei Zhao, Qinglan Li, Kuifeng Jin

“…The results of the case study also show that the 4DD algorithm filters out many observation gates close to the missing data or radar center, whereas the proposed algorithm tends to retain and correct these gates.…”
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