Some small-sample results on a bounded influence rank regression method

1995 ◽  
Vol 24 (4) ◽  
pp. 881-888 ◽  
Author(s):  
Rand R. Wilcox
Keyword(s):  
Regression Method ◽  
Small Sample ◽  
Rank Regression ◽  
Bounded Influence

The Accuracy Assessment of Weapon System Using Grey Linear Regression Method

2012 ◽  
Vol 268-270 ◽  
pp. 1809-1813
Author(s):  
Dai Yu Zhang ◽  
Bao Wei Song ◽  
Zhou Quan Zhu
Keyword(s):  
Linear Regression ◽  
Fuzzy Theory ◽  
Accuracy Assessment ◽  
Regression Method ◽  
Small Sample ◽  
Easy Access ◽  
Linear Regression Method ◽  
Grey Theory ◽  
Weapon System ◽  
Complex Engineering

The accuracy assessment of weapon system is always a complex engineering. How to make the most of the information given in only a few tests and obtain reasonable estimate is always a problem. Based on the fuzzy theory and grey theory, a grey linear regression method is presented. From the numerical example, we can see that this method provides an easy access to deal with data in small sample case and may have potential use in the analysis of weapon performance.

scholarly journals Study of oral lactobacillus towards developing a comprehensive structured for integrated exponential regression model

2020 ◽  
Vol 19 (3) ◽  
pp. 552-557
Author(s):  
RabiatulAdawiyah Abdul Rohim ◽  
Wan Muhamad Amir W Ahmad ◽  
Noor Huda Ismail ◽  
Muhammad Azeem Yaqoob ◽  
Mohammad Khursheed Alam ◽  
...  
Keyword(s):  
Regression Model ◽  
Bacterial Species ◽  
Research Work ◽  
Regression Method ◽  
Small Sample ◽  
Oral Disease ◽  
Probiotic Bacteria ◽  
Average Width ◽  
Exponential Regression ◽  
Exponential Regression Model

Introduction: Probiotics are well-defined as live microorganisms that usefully affect the host and probiotic bacteria have been used intensely. For years to target gastrointestinal disease by rebalancing the compound microflora. Besides the gastrointestinal tract also the oral cavity is highly colonized by bacteria and many different bacterial species are part of the microbiota in the mouth, as it offers ideal conditions for bacteria with a stable temperature, moist surface with a relatively stable pH and regular supply of nutrients. Probiotic bacteria like Lactobacillus are a promising treatment strategy for oral disease with a microbiological etiology. To gain better results, many researchers that study and emphasize specific methods been tried to build a new or improved methodology. Objectives: The aimed of this study is to improve the performance of exponential growth by adding bootstrap and fuzzy techniques (Integrated exponential regression method). The aim of the research work is to develop a comprehensive framework for an integrated exponential regression model. Material and Methods: The data were taken from the present data available from the recently done by a researcher for nurturing selected microorganisms. The gathered data will be used for the exponential modeling and the efficiency of the model will be compared accordingly due to the predicted interval from the exponential regression method and an integrated exponential regression method. This paper also provides the algorithm for the prediction of cell growth and inferences. Results: The result shows that the average width for the exponential regression model was 19.2228 while an integrated exponential regression method was 0.0075. The average width of integrated exponential regression was smaller than the exponential regression. This clearly shows that the integrated exponential regression method is more efficient than exponential regression technique. Conclusion: This proposed method can be applied to small sample size data, especially when limited data is obtained. Bangladesh Journal of Medical Science Vol.19(3) 2020 p.552-557

Projection Pursuit Regression Based on Hermite Polynomial for Atmospheric Corrosion Data Application

2014 ◽  
Vol 881-883 ◽  
pp. 1747-1753
Author(s):  
Wei Dong Nie ◽  
Xiao Ming Wang ◽  
Zhao Na Li ◽  
Xin Geng Li
Keyword(s):  
Atmospheric Corrosion ◽  
Hermite Polynomial ◽  
Influence Factors ◽  
Projection Pursuit ◽  
Steel Corrosion ◽  
Regression Method ◽  
Small Sample ◽  
Projection Pursuit Regression ◽  
Data Application ◽  
Corrosion Data

A Projection Pursuit Regression method by using Hermite Polynomial is put forward to make modeling and forcasting of corrosion data, because of small sample of acumulation data of metal material corrosion in atmosphere, Multi-dimensional Properties and Non-orthogonality of influence factors. Analyses and prediction of atmospheric corrosion data of a metal are made by using this method. Compared with PCA+SVM method, this method improves significantly the accuration of prediction and correctness of corrosion vehavior trend. The result proves that the Hermite Polynomial Projection Pursuit Regression method has great huge advantage in data analysis of steel corrosion in atmosphere.

Bounded Influence Rank Regression

1994 ◽  
Vol 56 (1) ◽  
pp. 209-220 ◽  
Author(s):  
J. D. Naranjo ◽  
T. P. Hettmansperger
Keyword(s):  
Rank Regression ◽  
Bounded Influence

Duration estimation model for bridge construction projects in Ghana

2017 ◽  
Vol 15 (6) ◽  
pp. 754-777 ◽  
Author(s):  
Gabriel Nani ◽  
Isaac Mensah ◽  
Theophilus Adjei-Kumi
Keyword(s):  
Construction Projects ◽  
Stepwise Regression ◽  
Regression Method ◽  
Small Sample ◽  
Percentage Error ◽  
Back Propagation Algorithm ◽  
Bridge Construction ◽  
Content Type ◽  
Feed Forward Back Propagation ◽  
Contract Duration

Purpose A major concern for construction professionals at the rural road agency in Ghana is the problem of fixing contract duration for bridge construction projects in rural areas. The purpose of the study was to develop a tool for construction professionals to forecast duration for bridge projects. Design/methodology/approach In all, 100 questionnaires were distributed to professionals at the Department of Feeder Roads to ascertain their views on the work items in a bill of quantities (BOQ) that impact significantly on the duration of bridge construction projects. Historical data for 30 completed bridge projects were also collected from the same Department. The data collected were executed work items in BOQ and actual durations used in completing the works. The qualitative data were analysed using the relative importance index and the quantitative data, processed and analysed using both the stepwise regression method and artificial neural network (ANN) technique. Findings The identified predictors, namely, in-situ concrete, weight of prefabricated steel components, gravel sub-base and haulage of aggregates, used as independent variables resulted in the development of a regression model with a mean absolute percentage error (MAPE) of 25 per cent and an ANN model with a feed forward back propagation algorithm with an MAPE of 26 per cent at the validation stage. The study has shown that both regression and ANN models are appropriate for predicting the duration of a new bridge construction project. Research limitations/implications The predictors used in the developed models are limited to work items in BOQs only of completed bridge construction projects as well as the small sample size. Practical implications The study has developed a working tool for practitioners at the agency to forecast contract duration for bridge projects prior to its commencement. Originality value The study has quantified the relationship between the work items in BOQs and the duration of bridge construction projects using the stepwise regression method and the ANN techniques.

scholarly journals Association between Homocysteine-Related Dietary Patterns and Gestational Diabetes Mellitus: A Study Using the Reduced Rank Regression Method

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Hui W ◽  
◽  
Yu-Hong L ◽  
Ling-Peng L ◽  
Min-Hui Y ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Dietary Patterns ◽  
Regression Method ◽  
Leafy Vegetables ◽  
Meat Intake ◽  
Rank Regression ◽  
Green Leafy Vegetables ◽  
Mode 2

Background: This study aimed to evaluate the association between homocysteine-related dietary patterns and gestational diabetes mellitus. Methods: A total of 488 pregnant women at 24-28 weeks of gestation between January 2019 and December 2020 were included. Demographic characteristics, dietary intake, and multivitamin supplement intake information were collected using a food frequency questionnaire (FFQ); fasting venous blood samples were collected for serum index detection. Serum homocysteine (Hcy), folic acid, and B12 were selected as response variables, and hyperhomocysteinemia (hHcy)- related dietary patterns were extracted using the descending rank regression method. The relationship between the score of hHcy-related dietary patterns and GDM was analyzed using a multivariate logistic regression model. Results: Three hHcy-related dietary patterns were extracted: (mode 1) more meat, cattle meat intake, green leafy vegetables, dark vegetables and soy, and less consumption of shrimp. (mode 2) livestock meat, eggs and more grains, green leafy vegetables, bacteria, algae, dairy, and less nuts intake; and (model 3) livestock meat intake, and less soy intake. Because the explanatory variation of mode 3 was relatively small, it was not retained. Only mode 2 had a positive and significant relationship with the risk of developing GDM. After adjusting for confounding factors, the risk of GDM was significantly increased in the highest quartile array (OR=2.96, 95% Confidence Interval: 0.939-9.356, P=0.004). There was no significant correlation between dietary pattern 1 and GDM risk (P >0.05).

Bounded influence nonlinear signed-rank regression

2012 ◽  
Vol 40 (1) ◽  
pp. 172-189 ◽  
Author(s):  
Huybrechts F. Bindele ◽  
Asheber Abebe
Keyword(s):  
Rank Regression ◽  
Signed Rank ◽  
Bounded Influence
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