A new method on treating missing values in polynomial wavelet regression

2011 ◽  
Author(s):  
Alsaidi M. Altaher ◽  
Mohd Tahir Ismail
Keyword(s):  
Missing Values ◽  
New Method ◽  
Wavelet Regression

scholarly journals Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies

2013 ◽  
Vol 03 (04) ◽  
pp. 26-40 ◽  
Author(s):  
Christophe Genolini ◽  
René Écochard ◽  
Hélène Jacqmin-Gadda
Keyword(s):  
Longitudinal Studies ◽  
Missing Values ◽  
New Method

Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal  

2021 ◽  
Author(s):  
Boli Yang ◽  
Yan Feng ◽  
Ruyin Cao
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Missing Values ◽  
Similarity Index ◽  
Visual Assessment ◽  
New Method ◽  
Landsat Data ◽  
Cloud Detection ◽  
Cloud Removal ◽  
Ultimate Objective

<p>Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     </p><p><strong> </strong></p><p><strong>Keywords: </strong>Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN</p>

scholarly journals ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Matúš Medo ◽  
Daniel M. Aebersold ◽  
Michaela Medová
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Missing Values ◽  
Differential Expression Analysis ◽  
New Method ◽  
Random Numbers ◽  
Differentially Expressed Proteins ◽  
Analysis Method ◽  
Proteomic Data ◽  
Python Package

Abstract Background Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as “imputation”, avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis. Results We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method. Conclusions The new method for the differential expression analysis of proteomic data is available as an easy to use Python package.

CopyMean: A new method to predict monotone missing values in longitudinal studies

2016 ◽  
Vol 132 ◽  
pp. 29-44 ◽  
Author(s):  
Christophe Genolini ◽  
Amandine Lacombe ◽  
René Écochard ◽  
Fabien Subtil
Keyword(s):  
Longitudinal Studies ◽  
Missing Values ◽  
New Method

scholarly journals Data reconstruction of flow time series in water distribution systems – a new method that accommodates multiple seasonality

2016 ◽  
Vol 19 (2) ◽  
pp. 238-250 ◽  
Author(s):  
Rui Barrela ◽  
Conceição Amado ◽  
Dália Loureiro ◽  
Aisha Mamade
Keyword(s):  
Time Series ◽  
Network Flow ◽  
Distribution Systems ◽  
Water Distribution ◽  
Missing Values ◽  
Window Size ◽  
Flow Time ◽  
New Method ◽  
Data Reconstruction ◽  
Flow Data

The purpose of this paper is to present a simple yet highly effective method to reconstruct missing data in flow time series. The presence of missing values in network flow data severely restricts their use for an adequate management of billing systems and for network operation. Despite significant technology improvements, missing values are frequent due to metering, data acquisition and storage issues. The proposed method is based on a weighted function for forecast and backcast obtained from existing time series models that accommodate multiple seasonality. A comprehensive set of tests were run to demonstrate the effectiveness of this new method and results indicated that a model for flow data reconstruction should incorporate daily and seasonal components for more accurate predictions, the window size used for forecast and backcast should range between 1 and 4 weeks, and the use of two disjoint training sets to generate flow predictions is more robust to detect anomalous events than other existing methods. Results obtained for flow data reconstruction provide evidence of the effectiveness of the proposed approach.

Selective Sampling and Orientation of Non-Homogeneous Tissues

1968 ◽  
Vol 26 ◽  
pp. 98-99
Author(s):  
C. C. Clawson ◽  
L. W. Anderson ◽  
R. A. Good
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Light Microscopy ◽  
Random Sampling ◽  
New Method ◽  
Microscope Examination ◽  
Small Areas ◽  
Selective Sampling ◽  
Large Numbers ◽  
Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

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