Standard Pearson Correlation Coefficient Research Articles

Location mapping for constructing biomass power plant using multi-criteria decision-making method

The use of renewable energy globally has shown to be one of the most promising strategies to move towards sustainable development. Biomass has become one of the world's most practical renewable energy sources. Decisions on bioenergy are difficult since the field crosses several Ministries' jurisdictions, including energy, agriculture, the environment, and industry and commerce. The biomass feedstocks are geographically and spatially distributed. Therefore, identifying and prioritizing suitable places for building expensive biomass plants is a complex problem for maximum productivity and return. This research provides an automated Multi-Criteria Decision Making (MCDM) technique with geographical information system (GIS) to solve the intricate nature of location identification and prioritization difficulty caused by the availability of numerous indicators, such as economic and environmental technical, social, and risk criteria. This research implements the Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) method and compared the performance using standard TOPSIS, ELECTRE, and SAW methods. A standardization procedure has been applied to entail constructing an overall performance index to assess the outcomes. Eventually, the standard Pearson correlation coefficient is taken a gander at reciprocal connections amongst multi-criteria decision-making approaches. The F-TOPSIS outperforms the other methods with the highest performance ratio of 98.78%, fuel consumption ratio of 15.1%, sensitivity ratio of 92.8%, mean absolute error rate of 21.6%, mean square error rate of 23.1%, transportation cost of 25.8%, and decision-making ratio of 94.7% when compared to others. The comparative results indicate that a multi-criteria decision-making process is a valuable tool for finding suitable biomass locations.

Evaluation of Pearson correlation coefficient and Parisi parameter of replica symmetry breaking in a hybrid electronically addressable random fiber laser.

The hybrid electronically addressable random (HEAR) laser is a novel type of random fiber laser that presents the remarkable property of selection of the fiber section with lasing emission. Here we present a joint analysis of the correlations between intensity fluctuations at distinct wavelengths and replica symmetry breaking (RSB) behavior of the HEAR laser. We introduce a modified Pearson coefficient that simultaneously comprises both the Parisi overlap parameter and standard Pearson correlation coefficient. Our results highlight the contrast between the correlations and presence or not of RSB phenomenon in the spontaneous emission behavior well below threshold, replica-symmetric ASE regime slightly below threshold, and RSB phase with random lasing emission above threshold. In particular, in the latter we find that the onset of RSB behavior is accompanied by a stochastic dynamics of the lasing modes, leading to competition for gain intertwined with correlation and anti-correlation between modes in this complex photonic phase.

Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.

Bounded-observation Kalman filtering of correlation in multivariate neural recordings.

A persistent question in multivariate neural signal processing is how best to characterize the statistical association between brain regions known as functional connectivity. Of the many metrics available for determining such association, the standard Pearson correlation coefficient (i.e., the zero-lag cross-correlation) remains widely used, particularly in neuroimaging. Generally, the cross-correlation is computed over an entire trial or recording session, with the assumption of within-trial stationarity. Increasingly, however, the length and complexity of neural data requires characterizing transient effects and/or non-stationarity in the temporal evolution of the correlation. That is, to estimate dynamics in the association between brain regions. Here, we present a simple, data-driven Kalman filter-based approach to tracking correlation dynamics. The filter explicitly accounts for the bounded nature of correlation measurements through the inclusion of a Fisher transform in the measurement equation. An output linearization facilitates a straightforward implementation of the standard recursive filter equations, including admittance of covariance identification via an autoregressive least squares method. We demonstrate the efficacy and utility of the approach in an example of multivariate neural functional magnetic resonance imaging data.

Measuring correlations between non-stationary series with DCCA coefficient

In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured by the fractional differencing parameter d). For a comparison, we also report the results for the standard Pearson correlation coefficient. The DCCA coefficient dominates the Pearson coefficient for non-stationary series.

A Study on the Relationship between English Vocabulary Threshold and Word Guessing Gtrategy for Pre-University Chinese Students in Malaysia

This survey aims at studying the relationship between English vocabulary threshold and word guessing strategy that is used in reading comprehension learning among 80 pre-university Chinese students in Malaysia. T-test is the main statistical test for this research, and the collected data isanalysed using SPSS. From the standard deviation test results, the large of vocabulary is proportional to the higher score of the reading test. The standard Pearson correlation coefficient of .750, and P< .01 indicated that there is a positive relationship between word guessing and vocabulary learning. T-test results showed that the vocabulary of the students should reach about 3500 words, only then can the students consciously and flexibly use the word guessing strategy in the reading process. Based on the students’ different vocabulary level, the English teacher should provide opportunities for more learning.

Bootstrap standard error and confidence intervals for the correlations corrected for indirect range restriction

The standard Pearson correlation coefficient, r, is a biased estimator of the population correlation coefficient, ρ(XY) , when predictor X and criterion Y are indirectly range-restricted by a third variable Z (or S). Two correction algorithms, Thorndike's (1949) Case III, and Schmidt, Oh, and Le's (2006) Case IV, have been proposed to correct for the bias. However, to our knowledge, the two algorithms did not provide a procedure to estimate the associated standard error and confidence intervals. This paper suggests using the bootstrap procedure as an alternative. Two Monte Carlo simulations were conducted to systematically evaluate the empirical performance of the proposed bootstrap procedure. The results indicated that the bootstrap standard error and confidence intervals were generally accurate across simulation conditions (e.g., selection ratio, sample size). The proposed bootstrap procedure can provide a useful alternative for the estimation of the standard error and confidence intervals for the correlation corrected for indirect range restriction.

Multi‐scale correlations between topography and vegetation in a hillside catchment of Honduras

All systems have causes and effects that can be appreciated at different spatial scales. Understanding and representing the complexity of multi‐scale patterns in maps and spatial models are key research objectives. We describe the use of three types of correlation analyses: (1) a standard Pearson correlation coefficient, (2) a ‘global’ multi‐scale correlation, and (3) local geographically weighted correlation. These methods were applied to topographic and vegetation indices in a small catchment in Honduras that is representative of the country's hillsides agro‐ecosystem which suffers from severe environmental degradation due to land‐use decisions that lead to deforestation, overgrazing, and unsustainable agricultural. If the geographical scale at which topography matters for land‐use allocation can be determined, then integration of knowledge systems can be focused. Our preliminary results show that: (1) single‐scale correlations do not adequately represent the relationship between NDVI and topographic indices; (2) peaks in the global multi‐scale correlations in agricultural areas coincided with the median farm size, but there was no evidence of any community or larger‐scale land‐use planning or optimization; and (3) local multi‐scale correlations varied considerably from the global results at all scales, and these variations have a strong spatial structure which may indicate local optimization of land use.

Membrane-associated STAT3 and PY-STAT3 in the Cytoplasm

Signal transduction from the plasma membrane to the nucleus by STAT proteins is widely represented as exclusively a soluble cytosolic process. Using cell-fractionation methods, we observed that approximately 5% of cytoplasmic STAT3 was constitutively associated with the purified early endosome (EE) fraction in human Hep3B liver cells. By 15-30 min after interleukin-6 (IL-6) treatment, up to two-thirds of cytoplasmic Tyr-phosphorylated STAT3 can be associated with the purified early endosome fraction (Rab-5-, EEA1-, transferrin receptor-, and clathrin-positive fraction). Electron microscopy, immunofluorescence, and detergent dissection approaches confirmed the association of STAT3 and PY-STAT3 with early endosomes. STAT3 was constitutively associated with clathrin heavy chain in membrane and in the 1- to 2-MDa cytosolic complexes. The membrane association was dynamic in that, within 15 min of treatment with the vicinal-thiol cross-linker phenylarsine oxide, there was a dramatic increase in bulk STAT3 association with sedimentable membranes. The functional contribution of PY-STAT3 association with the endocytic pathway was evaluated in transient transfection assays using IL-6-inducible STAT3-reporter-luciferase constructs and selective regulators of this pathway. STAT3-transcriptional activation was inhibited by expression constructs for dominant negative dynamin K44A, epsin 2a, amphiphysin A1, and clathrin light chain but enhanced by that for the active dynamin species MxA. Taken together, these studies emphasize the contribution of the endocytic pathway to productive IL-6/STAT3 signaling.

Bootstrap standard error and confidence intervals for the correlation corrected for range restriction: a simulation study.

The standard Pearson correlation coefficient is a biased estimator of the true population correlation, rho, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, rc, has been recommended, and a standard formula based on asymptotic results for estimating its standard error is also available. In the present study, the bootstrap standard-error estimate is proposed as an alternative. Monte Carlo simulation studies involving both normal and nonnormal data were conducted to examine the empirical performance of the proposed procedure under different levels of rho, selection ratio, sample size, and truncation types. Results indicated that, with normal data, the bootstrap standard-error estimate is more accurate than the traditional estimate, particularly with small sample size. With nonnormal data, performance of both estimates depends critically on the distribution type. Furthermore, the bootstrap bias-corrected and accelerated interval consistently provided the most accurate coverage probability for rho.