On the Predictability of Kibbutz Financial Distress: A Principal Component Analysis with Bootstrap Confidence Intervals

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.

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The Uncertainty and Robustness of the Principal Component Analysis as a Tool for the Dimensionality Reduction

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.235.1 ◽

2015 ◽

Vol 235 ◽

pp. 1-8

Author(s):

Jacek Pietraszek ◽

Ewa Skrzypczak-Pietraszek

Keyword(s):

Experimental Data ◽

Principal Component Analysis ◽

Dimensionality Reduction ◽

Confidence Intervals ◽

Dimensional Space ◽

Experimental Studies ◽

Principal Component ◽

Component Analysis ◽

High Dimensional ◽

Data Points

Experimental studies very often lead to datasets with a large number of noted attributes (observed properties) and relatively small number of records (observed objects). The classic analysis cannot explain recorded attributes in the form of regression relationships due to lack of sufficient number of data points. One of method making available a filtering of unimportant attributes is an approach known as ‘dimensionality reduction’. Well-known example of such approach is principal component analysis (PCA) which transforms the data from the high-dimensional space to a space of fewer dimensions and gives heuristics to select least but necessary number of dimensions. Authors used such technique successfully in their previous investigations but a question arose: whether PCA is robust and stable? This paper tries to answer this question by re-sampling experimental data and observing empirical confidence intervals of parameters used to make decision in PCA heuristics.

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Selection Of Variables And Indicators In Financial Distress Prediction Model-Svm Method Based On Sparse Principal Component Analysis

2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) ◽

10.1109/icwapr51924.2020.9494625 ◽

2020 ◽

Author(s):

Sen Zeng ◽

Wanjun Yang

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Financial Distress ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis ◽

Financial Distress Prediction ◽

Selection Of Variables ◽

Distress Prediction ◽

Selection Of

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A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽

10.1024/0301-1526/a000218 ◽

2012 ◽

Vol 41 (5) ◽

pp. 333-342 ◽

Cited By ~ 3

Author(s):

Kirchberger ◽

Finger ◽

Müller-Bühl

Keyword(s):

Principal Component Analysis ◽

Intermittent Claudication ◽

Completion Time ◽

Short Form ◽

Principal Component ◽

Component Analysis ◽

German Version ◽

Average Completion Time ◽

Sf 36 ◽

Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbachs alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbachs alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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