A New Method for Estimating Life Distributions from Incomplete Data.

1980 ◽  
Author(s):  
John Kitchin ◽  
Naftali A. Langberg ◽  
Frank Proschan
Keyword(s):  
Incomplete Data ◽  
New Method ◽  
Life Distributions

A NEW-METHOD FOR ESTIMATING LIFE DISTRIBUTIONS FROM INCOMPLETE DATA

1983 ◽  
Vol 1 (3) ◽  
Author(s):  
John Kitchin ◽  
Naftali A. Langberg ◽  
Frank Proschan
Keyword(s):  
Incomplete Data ◽  
New Method ◽  
Life Distributions

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

New method of mining incomplete data

2013 ◽  
Vol 30 (4) ◽  
pp. 411-416
Author(s):  
Lunwen Wang ◽  
Xianji Zhang ◽  
Lunwu Wang ◽  
Lin Zhang
Keyword(s):  
Incomplete Data ◽  
New Method

A Method for Incomplete Data Filling Based on Power Graph in IoT

2013 ◽  
Vol 380-384 ◽  
pp. 2431-2434
Author(s):  
Yang Yang ◽  
Xiao Wen ◽  
Peng Tang ◽  
Fang Yi Liu
Keyword(s):  
Internet Of Things ◽  
Incomplete Data ◽  
Experimental Results ◽  
New Method ◽  
The Internet ◽  
Improved Method ◽  
Power Graph ◽  
Network Fault ◽  
The Internet Of Things

With the development of the Internet of Things, collected data from the Internet of Things include more and more incomplete data because of the network fault or the sensing terminal breakdown. A lot of incomplete data do harm to the IoT application and decision. For filling the incomplete data in IoT effectively, this paper presents a new method based on power graph, which first uses the graph power to abstract the important attributes of the objects. Then the proposed method fills the important attributes using improved method based on similarity. Experimental results show the effectiveness of our method, especially for filling the massive incomplete data in IoT.

A New Method of Estimating Standard Fertility Measures From Incomplete Data

1975 ◽  
Vol 41 (2) ◽  
pp. 182 ◽  
Author(s):  
Ansley J. Coale ◽  
Allan G. Hill ◽  
T. James Trussell
Keyword(s):  
Incomplete Data ◽  
New Method

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.

A new method for the determination of nitrates

1960 ◽  
Vol 23 ◽  
pp. 227-232 ◽  
Author(s):  
P WEST ◽  
G LYLES
Keyword(s):  
New Method
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