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Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results. [ABSTRACT FROM AUTHOR]

AbstractThe advent of industrial big data has provided an unprecedented opportunity to achieve a data-driven monitoring of large-scale complex processes. When a process involves massive categorical variables each evaluated by attribute levels rather than real numbers, which is common in modern manufacturing and service applications, the existing process monitoring methods typically fail in modeling the joint distribution of these categorical variables due to the curse of high dimensionality. To fill this research gap, we propose a novel data mining–based framework—a nonparametric method—for High-Dimensional (HD) categorical process monitoring. Specifically, a series of multiscale frequent patterns are particularly defined and quickly extracted to characterize both the significant individual behaviors and the major collective behaviors of HD categorical variables. Then all these discovered multiscale patterns, serving as informative surrogates of the original HD categorical data, are monitored sequentially from low scale to high scale via a principled and powerful multiple hypotheses testing procedure embedded with an alpha spending function and a false discovery rate approach. The superiority of our proposed method is validated extensively by numerical simulations and real case studies. It is capable of maintaining a desired false alarm rate when the process is normal and becoming very sensitive to many different kinds of process shifts. [ABSTRACT FROM AUTHOR]

Acute respiratory infection (ARI) is an important cause of consultation in the emergency room and depending on the general condition of the person; it can become complicated and lifethreatening. The general objective of this study is to identify the immunoserology of acute respiratory infections in children under 15 years of age in the city of Riobamba, Ecuador in 2023; it consists of a statistical study in 85 children and adolescents. This research made it possible to identify the presence or absence of acute respiratory infections. The infections found are provoked by viruses, atypical bacteria, or a combination of both pathogens. These diseases may have an atypical origin due to previously unknown or little-known pathogens. Another indetermination is caused by the lack of knowledge of how the individuals studied were infected. That is why we use Refined Neutrosophic Statistics to study the collected data. This theory allows us to make the study more flexible, where the indeterminacy caused by ignorance is included, either about the biological origin of the diseases or about the immunological origin in the transmission of the disease. Specifically, we use the chi-square test for Contingency Tables for data processing. [ABSTRACT FROM AUTHOR]

Collapsibility is a practical and useful technique for dimension reduction in multidimensional contingency tables. In this paper, we consider marginal log‐linear models for studying collapsibility and related aspects in such tables. These models generalize ordinary log‐linear and multivariate logistic models, besides several others. First, we obtain some characteristic properties of marginal log‐linear parameters. Then we define collapsibility and strict collapsibility of these parameters in a general sense. Several necessary and sufficient conditions for collapsibility and strict collapsibility are derived based on simple functions of only the cell probabilities, which are easily verifiable. These include results for an arbitrary set of marginal log‐linear parameters having some common effects. The connections of strict collapsibility to various forms of independence of the variables are explored. We analyze some real‐life datasets to illustrate the above results on collapsibility and strict collapsibility. Finally, we obtain a result relating parameters with the same effect, but different margins for an arbitrary table, and demonstrate smoothness of marginal log‐linear models under collapsibility conditions. [ABSTRACT FROM AUTHOR]

The interquartile range is a statistical measure well suited to describe the variability of the data at hand, both at the population level and for sample data. The interquartile range is particularly useful when the distribution of the data is asymmetric or irregularly shaped. Here, the use of the interquartile range is investigated when the main aim is to compare the variability of two distributions using two independent random samples, without the need to make any distributional assumptions. Several techniques are compared through numerical studies and real data examples, with a particular attention given to the use of sample quantiles based on the Harrel-Davis estimator or the quantile regression. [ABSTRACT FROM AUTHOR]

Traditional analysis of two-way contingency tables with explanatory and response variables focuses on the independence of two variables. However, if the variables do not show independence or a clear relationship, the analysis shifts to the degree of association. Various measures have been proposed to calculate the degree of association. One is the proportional reduction in variation (PRV) measure. This measure describes the PRV from the marginal distribution to the conditional distribution of the response variable. Although conventional PRV measures can assess the association of the entire contingency table, they cannot accurately assess the association for each explanatory variable. In this paper, we propose a geometric mean type of PRV (geoPRV) measure, which aims to sensitively capture the association of each explanatory variable to the response variable. Our approach uses a geometric mean, and enabling analysis without underestimating the values when the cells in the contingency table are partially biased. The geoPRV measure can be constructed using any function that satisfies specific conditions. This approach has practical advantages, and in special cases, conventional PRV measures can be expressed as geometric mean types. [ABSTRACT FROM AUTHOR]

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference [K. Chaung et al., Cell 186, 5440-5456 (2023)], we develop Optimized Adaptive Statistic for Inferring Structure (OASIS), a family of statistical tests for contingency tables. OASIS constructs a test statistic which is linear in the normalized data matrix, providing closed-form P-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finite-sample bounds correctly characterize the test statistic's P-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. Using OASIS, we develop a method that can detect SARS-CoV-2 and Mycobacterium tuberculosis strains de novo, which existing approaches cannot achieve. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single-cell RNA sequencing, where under accepted noise models OASIS provides good control of the false discovery rate, while Pearson's X² consistently rejects the null. Additionally, we show in simulations that OASIS is more powerful than Pearson's X² in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations. [ABSTRACT FROM AUTHOR]

Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions. Information about covariation can be learned from either a summary description of frequencies (i.e., contingency tables) or trial‐by‐trial experience (i.e., sampling individual instances). Two studies were conducted to investigate the impact of information learning mode (i.e., description vs. self‐truncated sampling) on covariation judgment and related decision. In each trial under the description condition, participants were presented with a contingency table with explicit cell frequencies, whereas in each trial under the self‐truncated sampling condition, participants were allowed to determine when to stop sampling instances and thus the actual sample size. To eliminate sampling error, an other‐yoked (i.e., between‐subject) design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self‐truncated sampling condition. Experiment 1 showed that the self‐truncated sampling condition led to more moderate covariation judgments than the description condition (i.e., a description–experience gap). Experiment 2 demonstrated further that the same gap extended to covariation‐related decisions in terms of relative contingent preference (RCP). Regressive frequency estimation under self‐truncated sampling appeared to underlie the consistent gaps found in the two studies, whereas the impact of regressive diagnosticity (i.e., the same sample of instances was viewed as less diagnostic under description than under self‐truncated sampling) or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data. Future research might examine alternative accounts of the observed gaps, such as the impacts of belief updating and stopping rule under self‐truncated sampling. [ABSTRACT FROM AUTHOR]

External quality assessment data of clinical microbiology obtained over ten years by the Colombian company PROASECAL in laboratories of seven countries in Central and South America are analyzed. The objective is to describe the quality of clinical microbiology results of the participants in the study period (2010–2021). This analysis includes the results of genus and species identification and proper reporting of both Gram staining and susceptibility testing. Data from 195 laboratories, which processed 31 bacteria, were analyzed. In this study, 32 % of laboratories were classified as good because they successfully determined the genus and species, 36 % fair, 12 % poor, and 20 % critical, to which recommendations for improvement were sent as part of the reports. In the analysis conducted for concordance between Genus, Morphology, and Gram, the success rates obtained are between 83 % and 94 %. Regarding antibiograms, the performance observed in Gram-positive bacteria ranges from 57 to 98 %, while in Gram-negative bacteria, it is between 82 and 98 %. The descriptive tools to explain external quality assessment results in clinical microbiology are limited to the percentage of successes in each item to be reported. For the analysis of the population data set, in this article, we propose to use contingency tables (including estimates of sensitivity, specificity, and predictive values) and equality of proportions tests as techniques that facilitate the interpretation of the data. On the other hand, the statistical analysis described here can be used as an analysis methodology for similar studies. [ABSTRACT FROM AUTHOR]

In recent years, some statistical process monitoring (SPM) approaches have been used to control contingency table-based processes. The common assumption in this research is that the Neighborhood cells of the contingency table are temporally independent. This paper develops a new approach based on the Context-Tree method and Kullback-Leibler (KL) statistic to monitor the multi-way contingency tables by considering the dependence between Neighborhood cells in Phase II. The proposed approach is evaluated by using some simulation studies. In addition, the efficiency of the proposed approach has been approved using other sensitivity analyses in some numerical examples by contingency tables with more rows and columns and contingency tables with more categorical variables. Results show that proposed statistics have suitable performance in detecting the out-of-control condition under different shifts in a multi-way contingency table. [ABSTRACT FROM AUTHOR]

Background: In forensic pathology, asphyxia presents a problem as frequently as it is thorny and challenging. Some knowledge in forensic pathology is still considered to be traditionally acquired and is not critically assessed with modern statistical or technical tools. In this study, we seek to examine the injuries that are considered to be typical of cases of hanging (neck lesions, haemorrhages, and pleural and epicardial petechiae). Methods: We evaluated whether there was, indeed, a statistically significant association between these injuries and deaths from hanging as compared to non-hanging deaths. We collected 399 cases (32 deaths by hanging and 367 cases of non-hangings), built contingency tables and performed chi-square tests for each variable (lesion) examined; we also analysed this association in various subgroups of the sample (according to sex, age and weight ranges). Results: Our results did not deviate from the expected outcome based on traditional knowledge, although they do provide a more detailed demonstration and clarification of traditional knowledge regarding this topic. Conclusions: These findings provide valuable insights for future discussion, examination and deepening of knowledge that is traditionally accepted but often undersupported in the literature. [ABSTRACT FROM AUTHOR]

Here we consider an application of the method of maximum entropy in the mean to solve an extension of the problem of finding a discrete probability distribution from the knowledge of its marginals. The extension consists of determining joint probabilities when, besides specifying the marginals, we specify the expected value of some given random variables. The proposed method can incorporate constraints as the the requirement that the joint probabilities have to fall within known ranges. To motivate, think of the marginal probabilities as demands or supplies, and of the joint probability as the fraction of the supplies to be shipped from the production sites to the demand sites, thus joint probabilities become transportation policies. Fixing the cost of a transportation policy is equivalent to requiring that the unknown probability yields a given value to some random variable, and prescribing the range for each unknown may have an economical interpretation. [ABSTRACT FROM AUTHOR]

The bilateral asymmetry of the morphotypes m1 of the root vole Alexandromys oeconomus Pallas, 1776 from different parts of the range has been analyzed from morphogenetic positions. The material from the collection of the Institute of Systematics and Ecology of Animals, Siberian Branch, Russian Academy of Sciences (Novosibirsk), is used. A total of 2314 pairs of morphotypes m1 have been studied on the left and right rami of the mandible of voles. Seventeen different morphotypes and 70 different combinations of them are identified, of which 13 are symmetrical and 57 are asymmetrical. The data of bilateral asymmetry of morphotypes for each sample is presented in the form of square contingency tables. Based on these tables, the information-entropy parameters of the samples are calculated, in accordance with which the eco-geographic factors of the habitat are put. The 2B–PLS method is applied to the matrices after standardization. Two significant directions of contingency variability of the information-entropy parameters of the samples and eco-geographic factors of the environment have been identified. The first direction, latitudinal, is associated with a decrease in the Shannon entropy and fluctuating asymmetry in the northern samples of the root vole. Thus, the northern samples have a higher degree of morphogenetic stability compared to the southern ones. The second—the longitude direction—is highly correlated with the increase in altitude above sea level from west to east and is accompanied by a decrease in the values of the Cohen coefficient, Shannon information, and an increase in fluctuating asymmetry. This indicates a lower degree of morphogenetic stability of the eastern samples compared to the western ones. The approach we use complements the classical concept of the "epigenetic landscape" with the ability to take into account the influence of the environment. This is important in the practical use of asymmetry indicators for assessing the "health of the environment," especially when determining the natural background for populations of the "ecological periphery" of the areal. [ABSTRACT FROM AUTHOR]

Analyses of frequencies are commonly done using a chi-square test. This test, derived from a normal approximation, is deemed generally efficient (controlling type-I error rates fairly well and having good statistical power). However, in the case of factorial designs, it is difficult to decompose a total test statistic into additive interaction effects and main effects. Herein, we present an alternative test based on the $G$ statistic. The test has similar type-I error rates and power as the former one. However, it is based on a total statistic that is naturally decomposed additively into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVAs. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. We also examine how to render plots of frequencies along with confidence intervals. Finally, quantifying effect sizes and planning statistical power are described under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models.

We assessed several agreement coefficients applied in 2x2 contingency tables, which are commonly applied in research due to dichotomization. Here, we not only studied some specific estimators but also developed a general method for the study of any estimator candidate to be an agreement measurement. This method was developed in open-source R codes and it is available to the researchers. We tested this method by verifying the performance of several traditional estimators over all possible configurations with sizes ranging from 1 to 68 (total of 1,028,789 tables). Cohen's kappa showed handicapped behavior similar to Pearson's r, Yule's Q, and Yule's Y. Scott's pi, and Shankar and Bangdiwala's B seem to better assess situations of disagreement than agreement between raters. Krippendorff's alpha emulates, without any advantage, Scott's pi in cases with nominal variables and two raters. Dice's F1 and McNemar's chi-squared incompletely assess the information of the contingency table, showing the poorest performance among all. We concluded that Cohen's kappa is a measurement of association and McNemar's chi-squared assess neither association nor agreement; the only two authentic agreement estimators are Holley and Guilford's G and Gwet's AC1. The latter two estimators also showed the best performance over the range of table sizes and should be considered as the first choices for agreement measurement in contingency 2x2 tables. All procedures and data were implemented in R and are available to download from Harvard Dataverse https://doi.org/10.7910/DVN/HMYTCK. [ABSTRACT FROM AUTHOR]

High-Dimensional (HD) processes have become prevalent in many data-intensive scientific domains and engineering applications. The monitoring of HD categorical data, where each variable of interest is evaluated by attribute levels or nominal values, however, has seldom been studied. As the joint distribution of HD categorical variables can be fully characterized by a high-way contingency table or a high-order tensor, we propose a Probabilistic Tensor Decomposition (PTD) which factorizes a huge tensor into a few latent classes (rank-one tensors) to dramatically reduce the number of model parameters. Moreover, to enable high interpretability of this latent-class-type PTD model, a novel polarization regularization is devised, which makes each latent class focus on only a few vital combinations of attribute levels of categorical variables. An Expectation-Maximization algorithm is designed for parameter estimation from a historical normal dataset in Phase I, and an exponentially weighted moving average control chart is built in Phase II to monitor the proportions of latent classes that act as surrogates for each original categorical vector. Extensive simulations and a real case study validate the superior inference and monitoring performance of our proposed efficient and interpretable method. [ABSTRACT FROM AUTHOR]

In the oral health literature, researchers sometimes report measures of association that are inappropriate for their study design. Clinicians using evidence to inform their practice should be able to interpret clinical study results on the basis of the types of measures of association, independent of what the researchers of a study reported. The authors summarized which measures of association can be derived from experimental and observational studies and how to interpret them in the context of different study designs. They also suggested how inferences can be made on the basis of particular designs. Measures of association derived from randomized controlled trials and cohort studies differ from those of case-control and cross-sectional studies. These differences can be attributed to the temporality between exposures and outcomes inherent in the respective study designs. Different measures of association reported from the same study may lead to different clinical decisions. Furthermore, the same measure of association with the same effect estimate derived from different study designs may contribute to different clinical decisions. Measures of association should be interpreted in the context of a particular study design. Study designs and specific measures of association should be considered when drawing conclusions from clinical studies. Clinicians using the literature to inform practice should be cognizant of measures of association reported for a particular study design and whether the authors have interpreted the measure of association correctly in the context of their chosen study design. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the impact of tax benefits on alternatives for the use of Oil Palm trunks (Elaeis guineensis) as a raw material of the timber industry to avoid environmental liabilities in the department of Ucayali, Peru. The purpose is to investigate and develop technological alternatives to take advantage and industrially use the trunks that fulfilled their productive cycle, considering them as industrial raw material rather than agricultural waste. Therefore, the technology will be developed or adapted to generate products with added value. For this purpose, a survey was designed to 167 palm growers who answered 4 questions with 5 possible answer options. In addition to the descriptive analysis obtained, the Chi-squared Pearson's test was applied to determine the homogeneity between the different fiscal variables with respect to the trunk utilization variable. The result was to reject the null hypothesis (H0) with a significance value of 0.000; that is to say, every pair of variables has not the same population distribution, nevertheless most of the interviewed choose for there are tax benefits in alternatives of use of oil palm trunks as a raw material of the timber industry in this Peruvian department. [ABSTRACT FROM AUTHOR]

This paper proposes new non-model based partial/conditional association measures for ordinal contingency table where the main interest is the association between two ordinal variables of interest, adjusting for the effect of the covariate(s). We first develop a new type of scores for the ordinal variables, subcopula scores, taking into account the ordering of the categories of the variables and derive the subcopula regressions describing the relationships between the ordinal variables and the covariate(s). The proposed subcopula scores and subcopula regressions are obtained from the subcopula which uniquely links the joint distribution for the ordinal variables with their marginal distributions. We then define the new partial/conditional association measures between two ordinal variables to be the correlations between two sets of subcopula scores for them adjusted by the subcopula regressions of two response variables on the covariate(s). We theoretically investigate the properties of the proposed partial/conditional measures and their corresponding estimators. Finally, the performance of the proposed measures are evaluated by simulation studies and real-data examples. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the relationships between three categorical variables, of which at least one is ordinal, from a graphical point of view and using also inferential tools. Three way Correspondence Analysis is a useful data science visualisation technique to find and display these relationships. This analysis, like the classical two-way analysis, cannot be applied in an efficient way in the presence of ordinal categorical variables because this characteristic is not taken directly into account by the Pearson's chi-square contingency coefficient. Taguchi (Statistical analysis, Maruzen, Tokyo, 1966, Igaku 29:806–813, 1974) introduced a statistic that considers the ordinal nature of a categorical variable using the cumulative frequency of the cells of the contingency table across this variable. He introduced it as a simple alternative to Pearson's statistic for ordered contingency tables. This index is also at the base of several Correspondence Analysis extensions that have been proposed in the literature. We have developed a multiple extension of Taguchi's index. An enhancement of Correspondence Analysis has also been developed based on decomposition of this index. An orthogonal decomposition of this new index has been introduced to test the statistical significance of each aggregated column category. Moreover, a confidence region for each row and aggregated column category of the table has been developed. An application has been developed to highlight the easy applicability and graphical reading of the results of our approach. In this study, we evaluate the relationships between the ranking of the Italian football "Serie A" championship of the last 10 seasons and a set of two factors defined by average percentage of ball possession and number of tags for each team. This new approach may represent a useful guide for researchers who graphically analyse ranking data. [ABSTRACT FROM AUTHOR]

Cancer arises from the complex interplay of various factors. Traditionally, the identification of driver genes focuses primarily on the analysis of somatic mutations. We describe a new method for the detection of driver gene pairs based on an epistasis analysis that considers both germline and somatic variations. Specifically, the identification of significantly mutated gene pairs entails the calculation of a contingency table, wherein one of the co-mutated genes can exhibit a germline variant. By adopting this approach, it is possible to select gene pairs in which the individual genes do not exhibit significant associations with cancer. Finally, a survival analysis is used to select clinically relevant gene pairs. To test the efficacy of the new algorithm, we analyzed the colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD) samples available at The Cancer Genome Atlas (TCGA). In the analysis of the COAD and LUAD samples, we identify epistatic gene pairs significantly mutated in tumor tissue with respect to normal tissue. We believe that further analysis of the gene pairs detected by our method will unveil new biological insights, enhancing a better description of the cancer mechanism. [ABSTRACT FROM AUTHOR]

The general concept of algebraic statistics is to employ algebra tools to provide a better view of the structure of statistical problems and to contribute to finding solutions. The algebraic and statistical mixture represented by the method of Markov basis for the independent model (MBIM) on the contingency table is considered highly efficient to study and analyze the target problem which is Rheumatoid arthritis. The verification of the proposed method is carried out by taking advantage of the effect of the entropy property by using the law of Renyi entropy to show which of the alternate matrices that exist within the fiber elements are more independent than others. [ABSTRACT FROM AUTHOR]

The aim of this study was to use Hierarchical Loglinear Model (HLLM) in the analysis of multiway frequency tables and to interpret the main and interaction effects of this model on suicide cases. The data set used in this study was taken from the Turkish Republic State Statistical Institute (TUIK). A total of 6479 cases in 2016 and 2018 years were used in this analysis and the analyzes were made by considering gender, year and age variables. As a result of HDLM analysis, Year, Gender and Age, which are the main effects in suicide cases, and the interactions of Year × Gender and Gender × Age were found significantly (P

This paper aims to identify risk factors and to determine the level of contamination of Pseudomonas aeruginosa by conventional Polymerase Chain Reaction (PCR) in beef commercialized in the markets of Huánuco, Peru. To meet this objective, an epidemiological study was carried out. The sample consisted of 300 biological analyses of beef meats in the markets of Huánuco. A sample was taken from each position, making a total of 300 during a few days. Observation guides were used in order to collect data. The determination of the presence of Pseudomonas aeruginosa was made by PCR. A statistical analysis was carried out using contingency tables and Chi-square Pearson's test, between the possible causes of contamination and the results obtained from the sample analysis. Pseudomonas aeruginosa was isolated in 69 samples out of a total of 300, corresponding to a frequency of 23%. Among the identified factors are: dirty uniform of another color (p=2.5469x10-13); dirty hands (p=0.000); work utensils in poor condition and dirty (p=3.4776x10-12); these factors were statistically significant to the frequency of Pseudomonas aeruginosa in beef. [ABSTRACT FROM AUTHOR]

This study aims to establish a relationship between HRM and innovation studies through human capital characteristics in order to achieve a better understanding of HRM's supportive role in innovation realization aligned with knowledge-based services. The study provides a new model of coordination between innovation modes of service sector firms and HR configuration adopted for managing people in these firms. By gathering data about the main variables from 132 Iranian knowledge-based service firms with more than 30 employees, all located in Tehran province (selected through systematic sampling), the status of every firm in the field of innovation mode and HR configuration was determined. The hypotheses were tested using contingency tables and binomial test. After testing hypotheses, it was found that the strongest alignment exists between market-oriented innovation and productivity-based HR configuration (at about 53%). The average value for the human capital characteristics in firms that had two mentioned statuses were more than another three statuses. To achieve ideal human capital management, managers of KBSFs need to pay more attention to the compatibility of their HR configurations with innovation modes of KBSFs in order to approach the satisfactory productivity level seen in such firms. [ABSTRACT FROM AUTHOR]

The purpose of the research is to evaluate and determine the magnitude of the socio-economic impact of artisan charcoal production on the quality of life of the population involved in the Manantay district of the Coronel Portillo province, Ucayali Region. The charcoal production has been a source of income since the wood boom, and this activity is practiced by small entrepreneurs, who were marginalized by pollution in urban areas of the Manantay District, hence, they choose to produce artisanal charcoal which is subject of the present study, for which it was carried out to estimate the trend of the productivity of artisanal charcoal in the district of Manantay, also, to determine the level of quality of life of the population involved in the production of artisanal charcoal and to determine the socio-economic impact of the artisanal production of charcoal in the district. We use a random sample consisting in 45 producers of charcoal, which responded a query based on a Likert scale. The results were represented in contingency tables and were processed with the support of the coefficient of Chi Square of Pearson. From the results obtained it is concluded that there is a socio-economic impact of the production of artisanal charcoal on the quality of life of the population involved in the Manantay district. [ABSTRACT FROM AUTHOR]

In this paper, we examine the persistence of large-cap equity mutual funds performance, market timing skills, and selectivity of fund managers in India. The study uses monthly data of net assets values (NAV), market capitalization, and price to book ratio. The sample data period is from January 2000 to December 2019. We employ the methodology of Jensen (1968), Fama-French (1993), and Carhart (1997) models for forming portfolios and mimicking portfolios. The results reveal that the benchmark market index outperforms mutual funds. Moreover, there is a little evidence showing that Indian fund managers who consistently work on large-cap equity funds can generate abnormal returns. [ABSTRACT FROM AUTHOR]

Several test statistics are available for testing the independence of categorical variables from two-way contingency tables. A vast majority of published articles used the Pearson’s chi-squared test for such purposes; however, this test statistic may lead to biased conclusions under certain conditions. Therefore, we aimed to compare the performance of test statistics via a comprehensive simulation study considering several factors in contingency tables. We also evaluated the performance of each test statistic on a real-life dataset. This study contributes to the literature guiding researchers to select an appropriate test statistic under different conditions.

Likert-type items (questions) are a widely used scale in questionnaire design. The “neutral” or “undecided” option may lead to misinterpretation and confusion about the results. This paper proposes two novel log-linear models to measure how much accumulation of the neutral option over the contingency tables at any question levels. These models also test the odds that a respondent’s level how far from the median. These models will help the researchers how to incorporate the neutral option in conceptual frameworks.

Background: In forensic pathology, asphyxia presents a problem as frequently as it is thorny and challenging. Some knowledge in forensic pathology is still considered to be traditionally acquired and is not critically assessed with modern statistical or technical tools. In this study, we seek to examine the injuries that are considered to be typical of cases of hanging (neck lesions, haemorrhages, and pleural and epicardial petechiae). Methods: We evaluated whether there was, indeed, a statistically significant association between these injuries and deaths from hanging as compared to non-hanging deaths. We collected 399 cases (32 deaths by hanging and 367 cases of non-hangings), built contingency tables and performed chi-square tests for each variable (lesion) examined; we also analysed this association in various subgroups of the sample (according to sex, age and weight ranges). Results: Our results did not deviate from the expected outcome based on traditional knowledge, although they do provide a more detailed demonstration and clarification of traditional knowledge regarding this topic. Conclusions: These findings provide valuable insights for future discussion, examination and deepening of knowledge that is traditionally accepted but often undersupported in the literature.

The statistical independence between the reproductive stock and its corresponding recruitment to a fishery was tested for 43 stocks of clupeids in the Ransom Myers database by using the chi-squared test of a contingency table. The method was validated by simulating stock and recruitment (SR) contingency tables for data close to hypothetical SR curves that represent several known phenomena. The clupeid SR data were grouped by the criteria of percentile points to build the contingency tables. The independence test of the clupeid stocks was rejected in only 15 cases, thus the independence hypothesis was sustained in the remaining 28 cases. This demonstrates that fitting an SR model in these clupeid stocks is not justified because their abundance could be affected by several causes other than reproductive stock fluctuations. [ABSTRACT FROM AUTHOR]

Blinding is a critical component in randomized clinical trials along with treatment effect estimation and comparisons between the treatments. Various methods have been proposed for the statistical analyses of blinding-related data, but there is little guidance for determining the sample size for this type of data, especially if blinding assessment is done in pilot studies. In this paper, we try to fill this gap and provide simple methods to address sample size calculations for a "new" study with different research questions and scenarios. The proposed methods are framed in terms of estimation/precision or statistical testing to allow investigators to choose the best suited method for their goals. We illustrate the methods using worked examples with real data.

Logistic regression models are a powerful research tool for the analysis of cross‐classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to their easy interpretation. In this article we present a distance‐based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table. Two configurations are estimated, one for the rows and one for the columns, as the categories of a polytomous predictor and a nominal response variable respectively, such that the local odds ratio and the distances between the predictor and response categories are inversely related. The associations in terms of the odds ratios, or the ratios of the odds to their geometric means, are interpreted through distances for the most common coding schemes of the predictor variable, and the relationship between the distances related to different codings is investigated in its full dimension. The performance of the estimation procedure is analysed with a Monte Carlo experiment. The interpretation of the model and its performance, as well as its comparison with a two‐step procedure involving first a logistic regression and then unfolding, is illustrated using real data sets. [ABSTRACT FROM AUTHOR]

This study presents the extension of generalized Poisson (GP-1 and GP-2) models for three-way contingency table. We assume a mixed systematic component of the log-linear models for contingency tables to produce a linear transformation for the link function of Generalized Linear Models (GLMs). Maximum likelihood estimation method was derived for the parameters estimates of the models. An over-dispersed malaria data of 2019 was considered for the study. The GP-1 and GP-2 models for three-way contingency table was used to model the data. Based on Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) goodness-of-fits measures, the GP-2 model outperformed the GP-1 model for three-way contingency table on malaria data. We found that some parameters of the full model were statistically significant as; malaria cases was sensitive to all ages considered in the study, and people were more infected with malaria in the month of April, June, and July 2019. [ABSTRACT FROM AUTHOR]