scholarly journals Establishment and Research on the Model of the Company's Financial Risk Warning Based on Principal Component Analysis and Logistic Regression

2015 ◽  
Author(s):  
JingJing Fu
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Financial Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Risk Warning

Prediction of the financial risk of Special Treatment of a Listed Pharmaceutical Company in China: A Principal Component Analysis (Preprint)

2021 ◽  
Author(s):  
Yiming Cai ◽  
Xueyan Hu ◽  
Yiwei Guo ◽  
Ni Yan ◽  
Wai-Kit Ming
Keyword(s):  
Principal Component Analysis ◽  
Pharmaceutical Company ◽  
Financial Distress ◽  
Financial Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Special Treatment ◽  
Pharmaceutical Companies ◽  
Risk Warning ◽  
Z Value

BACKGROUND The listed pharmaceutical industry of China is growing swiftly by about 10% interest per year. However, risk always keeps pace with improvements. In China, listed companies with significant financial distress or irregularities will be assigned a special treatment (ST) label indicating a risk warning. Recently, eight listed pharmaceutical companies with a ST sign are surviving with but present serious investment risks. OBJECTIVE This paper aimed to discover the most significant factors that cause conversion of a listed pharmaceutical company into an ST firm. Tailored approaches for protecting China’s listed pharmaceutical companies from financial risks are being developed in order to help this domain in profit. Besides, we also aimed to offer suggestions for investors for investigating Chinese listed pharmaceuticals with the goal of assisting the investors in making successful investments. METHODS After collecting data from online databases, a principal component analysis (PCA) model was applied for descending data dimensions. After selecting the components with highest contribution, a logistic regression (LR) model was conducted for simplifying the outcome and calculating the intercept, component coefficients, standard error (SE), and Z and p-values. RESULTS Nine principal components were crucial from the principal component analysis (PCA) model, and two components (components 1 and 5) remaining as the most important factors after the LR model. The estimated intercept was 4.866 (SE 1.096, Z-value 4.442, p < 0.001). The estimated coefficient for components 1 (SE 0.332, Z-value 3.067, p = 0.002) and 5 (SE 0.643, Z-value −2.6, p = 0.009) were 1.017 and −1.672, respectively. CONCLUSIONS Investors are supposed to supervise the accounting conditions in three sectors: (1) solvency, profitability, and research and development (R&D) investment; (2) running the firm properly; and/or (3) investing successfully. Firms are supposed to hire professional partitioners as leaders. The major shareholders should not plan any questionable investments for personal income, and they should ensure the firm works under conditions with low liability, high profitability, and R&D costs that match the perfect growth opportunity after the Coronavirus 2019 (COVID-19) pandemic and the strong growth of China’s economy in 2020.

scholarly journals Performance Analysis of Classifiers in Identifying NREM Sleep and Awaken Stages from EEG Signals

2020 ◽  
Vol 8 (6) ◽  
pp. 4321-4326
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Linear Regression ◽  
Expectation Maximization ◽  
Principal Component ◽  
Nrem Sleep ◽  
Component Analysis ◽  
Medical Procedure ◽  
Eeg Signals ◽  
Non Linear

Electroencephalogram is a medical procedure which helps in analyzing the activities of the brain through electrical signals. In this paper a simple classification technique of EEG signal into two stages as NREM sleep and awaken stages had been undertaken. Classifying these stages helps the physician to understand the patient's sleep disorder by knowing whether the person's brain is in NREM sleep or awaken stages. Physionet EEG signals are samples of 256 signals per second for 10 seconds duration is used in this work. Then the EEG samples properties are analyzed through various parameters like statistical features, entropy Pearson correlation coefficient, Power spectral density, scatter plots and Hilbert transform plots. The classification of NREM sleep and awaken stage is performed by the ten different classifiers broadly grouped into non linear and hybrid one. The classifiers used include Linear Regression, Non Linear Regression, Logistic Regression, Principal Component Analysis, Kernel Principal Component Analysis, Expectation Maximization, Compensatory Expectation Maximization, Expectation Maximization with Logistic Regression Compensatory Expectation Maximization with Logistic Regression, and Firefly. The performances of the classifiers are analyzed using regular parameters like sensitivity, accuracy, specificity, performance index. The highest accuracy of 95.575% is achieved with linear regression for awaken signal and an accuracy of 95.315% is achieved using kernel PCA for sleep signal.

Grading Sewing Operator Skill Using Principal Component Analysis and Ordinal Logistic Regression

2018 ◽  
Vol 9 (2) ◽  
pp. 28-44 ◽  
Author(s):  
Thanh Quynh Le ◽  
Nam Van Huynh
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Manufacturing Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Ordinal Logistic Regression ◽  
Skill Level ◽  
Production Operation ◽  
Skill Levels ◽  
Worker Skill

In the apparel manufacturing process, productivity and quality are somewhat determined by operator skill level. Predicting worker skill level is very important for effective production operation management. However, the current methods for ranking skill level in the manufacturing industry have been based on the subjective evaluation of managers and have failed both in predicting the operator skill level needed for planning and in encouraging operators to develop new skills for quality and productivity. This article develops a new method for grading sewing worker skill levels that employs updated knowledge from experts involved in training, coaching and managing operations in factories. This approach uses the Delphi method combined with principal component analysis to define and classify six qualitative variables that effect on three aspects of operator skill, including coordination skill, sustaining skill, and tool operating skill. Based on these three variables, ordinal logistic regression is applied to grade skill levels, with a statistically significance result.

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