Popular Statistical Measures Research Articles

Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan

This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models (Probit and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

Sensitivity of the RMSD for Detecting Item‐Level Misfit in Low‐Performing Countries

AbstractAlthough the root‐mean squared deviation (RMSD) is a popular statistical measure for evaluating country‐specific item‐level misfit (i.e., differential item functioning [DIF]) in international large‐scale assessment, this paper shows that its sensitivity to detect misfit may depend strongly on the proficiency distribution of the considered countries. Specifically, items for which most respondents in a country have a very low (or high) probability of providing a correct answer will rarely be flagged by the RMSD as showing misfit, even if very strong DIF is present. With many international large‐scale assessment initiatives moving toward covering a more heterogeneous group of countries, this raises issues for the ability of the RMSD to detect item‐level misfit, especially in low‐performing countries that are not well‐aligned with the overall difficulty level of the test. This may put one at risk of incorrectly assuming measurement invariance to hold, and may also inflate estimated between‐country difference in proficiency. The degree to which the RMSD is able to detect DIF in low‐performing countries is studied using both an empirical example from PISA 2015 and a simulation study.

Financial performance measures in credit scoring

In this paper, we suggest that financial performance of loans and loan portfolios, rather than statistical performance of scores, should be the critical focus for lenders and lending institutions using credit scoring to fund and acquire new borrowers. We propose two measures for measuring relative financial performance of loan accounts when portfolio acquisition decisions are based on risk assessments. These compare with numerous statistical performance measures for response and risk scores that assess borrower response to offers, default, bankruptcy, late payment and fraud. Popular statistical measures often used in scorecard development and validation testing include the K-S statistic, Gini coefficient, AUROC, ROC curves, divergence, and others. The measures that we suggest focus on relative return on equity (ROE) and market share; in our opinion, such financial performance measures are more meaningful to lenders and businesses than statistical measures of scorecard performance.

Similarity Measures for Enhancing Interactive Streamline Seeding

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.

A practical approach for risk‐adjusting performance

PurposeRisk management has grown increasingly popular in recent years due to the recognition that risk should be as actively managed as performance. A key objective of risk management is to evaluate performance in the context of the relative volatility in which business operations are undertaken. However, accomplishing this has generally proven difficult. This paper aims to present a practical approach for risk‐adjusting performance.Design/methodology/approachThis paper presents a practical risk‐adjustment methodology that is based on a popular statistical measure. The utility of the approach is demonstrated in two practical examples: the first is an industry example and the second is an M&A example.FindingsThe results of the research suggest that the risk‐adjustment approach presented here could become an important part of both performance management and risk management programs.Research implications/limitationsThe approach detailed in this paper facilitates the practical risk‐adjustment of select performance measures and risk measures. As this is an introductory paper, further research could be conducted on the specifics of the risk‐adjustment process as well as the strategic context in which measures are risk‐adjusted.Originality/valueThis paper introduces a practical approach of risk‐adjusting performance that was inspired by a popular statistical measure, which is demonstrated in two practical examples.

Obesity and prostate cancer – An ambiguous relationship

2007 Elsevier Ltd. All rights reserved. In industrialised countries where the eating habits of people, as well as their extent of exercise, have changed distinctly over the last decades, obesity has emerged as one of the leading health burdens. In Europe the prevalence of obesity has tripled since the 1980s. At present, between 30% and 80% of adults are overweight in most countries and estimates even indicate 20% obese adults in 2010 [http://www.euro.who.int/ obesity]. The definitions are based on the well-known body mass index (BMI). In detail, a BMI over 25 kg/m is defined as overweight and a BMI over 30 kg/m as obese. The BMI is a popular statistical measure as it is easy to use. However, the BMI may not fully reflect the disease-related dimensions of obesity since it does not address the allocation of the fat tissue. For example, central obesity (the so called apple-shaped form), which is best diagnosed by measuring the waist-hip ratio correlates much stronger with cardiovascular disease than BMI by itself. Hence, measurement of obesity can not be determined as easily as it appears at first glance. This fact is reflected by a wide variety of different methods. It is the general consent that obesity has significant health consequences. In particular, obese people are at a higher risk of certain chronic diseases such as type II diabetes, cardiovascular disease, hypertension and strokes. The same applies for several types of cancers, especially breast and large-bowel cancer. er Ltd. All rights reserved 0. i-graz.at. There is also some evidence of a relationship between prostate cancer and obesity. In 1997, by analysing about 135,000 men, Andersson et al. found that the risk of death from prostate cancer was statistically significant above the reference category in all BMI categories. Notably, the authors described a stronger relation to mortality rather than incidence in this context. This year, Wright et al. published an evaluation of 287,760 men. He could not find a positive correlation between prostate cancer incidence and obesity but noticed that a higher BMI and adult weight gain was linked to an increased risk of dying from prostate cancer. In contrast, Giovannucci et al. did an analysis of nearly 48,000 men and concluded that adult obesity was not an independent risk factor for prostate cancer. Here, tallness was a marker of increased risk while even greater adiposity between the ages of 5 and 20 years was associated with a lower risk of prostate cancer. However, several studies exist which show a direct relationship between BMI and the incidence of prostate cancer, whereas others demonstrate no clear association. The rationale for any link between obesity and prostate cancer may be based on the association of obesity with endocrine changes represented by decreased testosterone and increased oestrogen serum levels. Prostate cells are hormone dependent. Testosterone is necessary for differentiation and growth. Prostate cancer cells principally retain this behaviour.

Exploring texture ensembles by efficient Markov chain Monte Carlo-Toward a "trichromacy" theory of texture

Presents a mathematical definition of texture, the Julesz ensemble /spl Omega/(h), which is the set of all images (defined on Z/sup 2/) that share identical statistics h. Then texture modeling is posed as an inverse problem: Given a set of images sampled from an unknown /spl Omega/(h/sub */), we search for the statistics h/sub */ which define the ensemble. A /spl Omega/(h) has an associated probability distribution q(I; h), which is uniform over the images in /spl Omega/(h) and has zero probability outside. The authors previously (1999) showed q(I; h) to be the limit distribution of the FRAME (filter, random field, and minimax entropy) model, as the image lattice /spl Lambda//spl rarr/Z/sup 2/. This conclusion establishes the intrinsic link between the scientific definition of texture on Z/sup 2/ and the mathematical models of texture on finite lattices. It brings two advantages: the practice of texture image synthesis by matching statistics is put on a mathematical foundation; and we need not learn the expensive FRAME model in feature pursuit, model selection and texture synthesis. An efficient Markov chain Monte Carte algorithm is proposed for sampling Julesz ensembles. It generates random texture images by moving along the directions of filter coefficients and, thus, extends the traditional single site Gibbs sampler. We compare four popular statistical measures in the literature, in terms of their descriptive abilities. Our experiments suggest that a small number of bins in marginal histograms are sufficient for capturing a variety of texture patterns.