scholarly journals Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis

2019 ◽  
pp. 1-13
Author(s):  
Privatus Christopher
Keyword(s):  
Principal Component Analysis ◽  
Child Mortality ◽  
Vitamin A Deficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Set ◽  
Under Five ◽  
Long Time ◽  
Main Components

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.

scholarly journals Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis

2007 ◽  
Vol 7 (3) ◽  
pp. 875-886 ◽  
Author(s):  
T. W. Chan ◽  
M. Mozurkewich
Keyword(s):  
Principal Component Analysis ◽  
Size Distribution ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
New Method ◽  
Aerosol Size Distribution ◽  
Distribution Data ◽  
Complex Data ◽  
Data Set

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simple, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.

scholarly journals Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis

2006 ◽  
Vol 6 (5) ◽  
pp. 10463-10492
Author(s):  
T. W. Chan ◽  
M. Mozurkewich
Keyword(s):  
Principal Component Analysis ◽  
Size Distribution ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
New Method ◽  
Aerosol Size Distribution ◽  
Distribution Data ◽  
Complex Data ◽  
Data Set

Abstract. Principal component analysis provides a fast and robust method to reduce the data dimensionality of an aerosol size distribution data set. Here we describe a methodology for applying principal component analysis to aerosol size distribution measurements. We illustrate the method by applying it to data obtained during five field studies. Most variations in the sub-micrometer aerosol size distribution over periods of weeks can be described using 5 components. Using 6 to 8 components preserves virtually all the information in the original data. A key aspect of our approach is the introduction of a new method to weight the data; this preserves the orthogonality of the components while taking the measurement uncertainties into account. We also describe a new method for identifying the approximate number of aerosol components needed to represent the measurement quantitatively. Applying Varimax rotation to the resultant components decomposes a distribution into independent monomodal distributions. Normalizing the components provides physical meaning to the component scores. The method is relatively simply, computationally fast, and numerically robust. The resulting data simplification provides an efficient method of representing complex data sets and should greatly assist in the analysis of size distribution data.

scholarly journals Identification of informative performance traits in swine using principal component analysis

2005 ◽  
Vol 57 (6) ◽  
pp. 805-810 ◽  
Author(s):  
L. Barbosa ◽  
P.S. Lopes ◽  
A.J. Regazzi ◽  
S.E.F. Guimarães ◽  
R.A. Torres
Keyword(s):  
Principal Component Analysis ◽  
Litter Size ◽  
Average Daily Gain ◽  
Principal Component ◽  
Original Data ◽  
Relevant Information ◽  
Component Analysis ◽  
Data Set ◽  
Performance Traits ◽  
Eigen Values

Using principal component analysis, records of 435 animals from an F2 swine population were used to identity independent and informative variables of economically important performance. The following performance traits were recorded: litter size at birth (BL), litter size at weaning (WL), teat number (TN), birth weight (BW), weight at 21 (W21), 42 (W42), 63 (W63) and 77 (W77) days of age, average daily gain (ADG), feed intake (FI) and feed:gain ratio (FGR) from 77 to 105 days of age. Six principal components expressed variation lower than 0.7 (eigen values lower than 0.7) suggesting that six variables could be discarded with little information loss. The discarded variables present significant simple linear correlation with the retained variables. Retaining variables BL, TN, W77, FI and FGR and eliminating all the rest would retain most of the relevant information in the original data set.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

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