Partial Least Square

A method for rapid non-destructive detection of eugenol in caryophylli flos was developed with near-infrared spectroscopy and chemometrics. One hundred and three samples were collected, and gas chromatography with internal standard was used to determine the reference value of eugenol in caryophylli flos. Near-infrared spectra were recorded from their powders and pretreated with standard normal variate and 1st derivative, and then a model was built with the chemometrics method, partial least square. Outliers of samples were detected. When 6 factors were adopted in a model, the performance was found to be the best. For prediction set, the slope was 0.9461, the offset was 0.3008, R-square was 0.9388, root mean square error of prediction was 0.6987 %, bias was -0.2815 % and residual prediction deviation was 5.89. The statistical analysis showed the predicted results were consistent with the reference values. It is feasible that near-infrared spectroscopy could be used to rapidly and accurately detect the main active content, eugenol in caryophylli flos as a method of quality control.

Partial Least Square Multi-group-Analysis (PLS-MGA) can be known as moderating effect to moderates the causal effect between exogenous and endogenous constructs (Afthanorhan & Ahmad, 2014). Three approaches to multi-group analysis have been proposed within the PLS path modeling lately by infamous researchers whom expert in their fields likely parametric, nonparametric and permutation approaches. In the accordance of Keil et. al (2000), the standardized error or standardized deviation for each sample is prevailed to yield the outcome of probability value (p-value). As usual, p-value often is being used in statistical analysis to test the potential of our analysis. In other words, these values are deemed as a threshold to decide the significant of the case study applied.

PLS (Partial Least Square) is an efficient statistical method for predicting a small data sample with a lot of variables that might be correlated with each other. By doing a computer calculation, PLS becomes easier to be implemented for a great amount of data without the need to provide assumption (Wilks, 2006). In PLS, the dimensional

Testing models of behavior analysis performed with the approach of Structural Equation Model (SEM) is by using software Partial Least Square (PLS). PLS is a structural equation model (SEM) based components or variance ( variance ) which for present purposes is considered better than the other SEM techniques. Selection of PLS method is based on the consideration that all variables used in this study is a latent variable that can not be measured directly , except that in this study there are two latent variables were formed with formative and reflective indicators and the results of this study will be used to examine the relationship between latent variables by forming latent variable indicators . PLS also allows analysis at the same time over the latent variables with multiple indicators . While the study Chen et al . (in Ghozali, 2005) states when using a multiple regression testing must be done repeatedly so that for each indicator forming the dependent variable.

The model test was required before the third hypothesis test conducted. Test model of research in Partial Least Square-Structural Equation Modelling can be done through the outer and inner models. The result obtained by SmartPLS stated that loading factor values above 0.5 is required to established construct. Furthermore, the results presented in the table outer validity loading.

Walaupun Indonesia mempunyai bilangan penduduk Islam yang besar di dunia, namun penguasaan pasaran perbankan Islam di Indonesia cuma mewakili lima peratus daripada sistem perbankan keseluruhannya. Oleh itu, usaha untuk menarik lebih ramai pelanggan perbankan Islam adalah satu cabaran besar kepada bank-bank Indonesia. Beberapa kajian telah dilakukan dengan menggunakan Theory of Reasoned Action (TRA) dan Theory of Planned Behaviour (TPB) untuk mengenal pasti factor yang mempengaruhi niat pelanggan dan pemilihan bank. Kajian ini bertujuan untuk memenuhi jurang ini dengan mengkaji konstrak TRA dan TPB (sikap, norma subjektif) dengan tambahan lima pemboleh ubah baharu; penentuan harga produk dan perkhidmatan, agama, pengetahuan, sokongan teknologi dan sokongan kerajaan kepada niat pelanggan serta pemilihan bank Islam. Borang soal- selidik telah di agihkan kepada 375 orang responden yang terdiri daripada pelanggan bank di Barat, Tengah, dan wilayah Timur Indonesia. Dengan menggunakan Partial Least Square Structural Equation Modelling (PLS-SEM), model lanjutan menjelaskan 48.8 peratus variasi dalam niat. Sikap, norma subjektif, agama, pengetahuan dan sokongan kerajaan didapati mempunyai pengaruh yang signifikan dan positif ke atas niat kecuali harga. Menggunakan niat sebagai pengantara, model memaparkan 51 peratus daripada variasi dalam pemilihan bank. Hasilnya menunjukkan bahawa niat mempunyai kesan pengantara yang positif dan signifkan ke atas hubungan antara sikap, norma subjektif, agama, dan pengetahuan dengan pilihan bank Islam. Sokongan kerajaan didapati tidak signifikan tetapi berkaitan secara positif. Di bawah kesan pengantaraan, harga menjadi signifikan tetapi berkaitan secara negatif dengan pemilihan. Keputusan menunjukkan bahawa bank- bank bank Islam Indonesia harus menawarkan produk dan perkhidmatan pada harga yang kompetitif untuk menarik lebih ramai pelanggan. Lebih penting lagi, bank- bank Islam Indonesia perlu mempunyai tekad yang kuat untuk mewujudkan sikap pelanggan yang positif terhadap perbankan Islam melalui penyebaran pengetahuan yang lebih luas, rujukan rakan sebaya dan keluarga dan pematuhan Syariah. Kesimpulannya, keputusan PLS-SEM menyokong penggunaan TRA dan teori TPB untuk meningkat pemilihan perbankan Islam di Indonesia.

component analysis (PCA) is commonly used for this purpose in environmental research [7,11,12], but does not allow scientists to separate between the predictor and response variables [9]. Another statistical method, Exploratory factor analysis (EFA), uncovers the under- lying structure for large sets of variables based on the shared variances among factors [13,14], but is sensitive to sample size, i.e., the sample size must be at least three- fold higher than the number of variables [15]. Partial least square (PLS) may represent a solution where such multivariate methods fail. This technique is routinely used in chemometric analysis when a large number of independent variables (>1000) are obtained with respect to a small number of samples (10 to 100) [16]. Depend- ing on the objective of the study, PLS can serve either as a principal component technique, correlation tech- nique, path modeling technique, or canonical correlation technique [17]. Overall, we suggest that this statistical method is a potential approach in environmental moni- toring surveys for exploratory modeling of data sets with a large number of variables, but a moderate sample size (n = 20–50). Such situations are often encountered in baseline field surveys, which document the environmen- tal conditions that exist at a specific moment in time to provide background in case of unknown changes in the future [18].

Introduction: Knowledge, attitude and practices regarding dengue are latent variables which are substantiated through manifest variables. The manifest variables that form the indicative construct of knowledge, attitude and practice can be factored into sub-constructs such that the impact of each indicative variable can be verified. Method: Evaluation of the sub-constructs of knowledge, attitude and practices regarding dengue using a Partial least square path models with R programming language.

Theories are developed to explain an observed phenomenon in an effort to understand why and how things happen. Theories thus, use latent variables to estimate conceptual parameters. The level of abstraction depends, partly on the complexity of the theoretical model explaining the phenomenon. The conjugation of directly-measured variables leads to a formation of a first- order factor. A combination of theoretical underpinnings supporting an existence of a higher- order components, and statistical evidence pointing to such presence adds advantage for the researchers to investigate a phenomenon both at an aggregated and disjointed dimensions. As partial least square (PLS) gains its tractions in theory development, behavioural accounting discipline in general should exploit the flexibility of PLS to work with the higher-order factors. However, technical guides are scarcely available. Therefore, this article presents a PLS approach to validate a higher-order factor on a statistical ground using accounting information system dataset.

developing. The objective of this study is to examine local resources that the Community Learning Center (Pusat Kegiatan Belajar Masyarakat or PKBM) can benefit from; this is to improve the competitiveness of such resources. This study was conducted in Gorontalo Province, Indonesia. The explanatory survey method was employed to explore the issue. This present study employed partial least square method to examine the interrelation among resources, strategy, and the superiority of the institution. The results reveal that the local resources measured by natural resources, human resources, and cultural resources are scarce, irreplaceable, satisfying the needs of the community education, as well as easy to organize. On the other hand, the level of the excellence of the institution is considerably high based on the absorption of graduates. The result of verification test shows that the local resources within the environment are able to improve its excellence by applying the strategy of community learning center.

Chinese tax revenue is non-linear and coupled, and is influenced by many factors. Therefore, traditional forecasting methods are not sufficient to predict the value of it. In this paper, disadvantages of the existing forecasting methods are analyzed. Then partial least square support vector machine (PLS-SVR) is used to construct a tax revenue prediction model. An improved particle swarm algorithm is used to optimize the parameter set of (C, σ2), which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and multi-factors of tax revenue, and ensure stability and accuracy of support vector machine based regression. Case study on Chinese tax revenue during the last 30 years demonstrates that the optimized PLS-SVR model is much more accurate than other prediction methods.

Entrepreneurial attitude orientation model in coincidence with e-entrepreneurial intention support that model works in building undergraduates intention toward starting online business. Statistical facts revealed that having more perception about control and influence over business will have greater intention toward starting their online business. In support of self-esteem and creativity, undergraduates who have more intention toward starting a business in a unique way are those who have competency and self-confidence in their ability. So, the findings of the study are similar with the existing literature on self- employment intention by using the EAO model (Ismail et al. 2013; Shariff and Saud 2009; Nga and Shamuganthan 2010). H5 self-efficacy as the contribution to the EAO model shows positive significant relation with e-entrepreneurial intention with β = 0.3956, t > 1.96. By 100 % changing the aspect of self-efficacy will bring 39.56 % change in the e- entrepreneurial intention. Accordingly, H5 is accepted for the study. As variance accounted for proves full mediation exist between creativity and e-entrepreneurial intention through self-efficacy. The relationship between creativity and self-efficacy with β of 0.5655 and t > 1.96 proves that 100 % change in innovation will bring 55.56 % mediation effect of self- efficacy. In the view that H6 is accepted, variance accounted for and mediation analysis in partial least square extend the EAO model with the mediation role of self-efficacy between innovation and e-entrepreneurial intention. Previous research suggested that entrepreneur- ship is influenced by innovation, but in this model, the relation is largely mediated by self-

Knowledge and acceptance of the pros and cons of GST implementation are thus critical in determining how the general public would perceive the tax reform and the impacts on the quality of life. Thus, the objective of the current study is to explore the relationship between acceptance and knowledge levels of Malaysians as well as their feelings towards the implementation of GST and the effect on quality of life, using the Partial Least Square approach. Findings from the study may indicate whether or not the government’s efforts in communicating the importance of the GST are effective in enhancing the public’s acceptance and knowledge regarding the tax reform. A significant positive finding towards the quality of life would exhibit that positive feelings towards the GST entail greater potential success in improving the quality of life in Malaysia.

overnment of India’s push towards digital transactions after demonitisation has led to flooding of financial mobile apps of banks and private corporations in the app stores. Among the panoply of financial mobile apps, few apps standout due to their high downloads. Based on downloaded data, the major financial mobile apps include BHIM promoted by National Payments Corporation of India, Paytm promoted by One97 Communications Ltd & Alibaba Group Holdings Ltd, and PhonePe promoted by Flipkart Online Services Pvt. Ltd & Yes Bank Ltd. One of the key aspects of financial transaction through a mobile app is trust. Without trust, the consumer will not transact through his mobile. This research paper uses Partial Least Square method to understand the variables that form the basis of trust in financial mobile apps. The research helps in identifying variables which should be tweaked by the companies to improve trust in their mobile apps.

Maximum relevance and minimum redundancy (mRMR) has been well recognised as one of the best feature selection methods. This paper proposes a Kernel Partial Least Square (KPLS) based mRMR method, aiming for easy computation and improving classification accuracy for high-dimensional data. Experiments with this approach have been conducted on seven real-life datasets of varied dimensionality and number of instances, with performance measured on four different classifiers: Naive Bayes, Linear Discriminant Analysis, Random Forest and Support Vector Machine. Experimental results have exhibited the advantage of the proposed method over several competing feature selection techniques.

Abstract Given the economic realities in Nigeria, the country must constantly create new jobs, and diversify the industrial and commercial sector to take advantage of human and natural resources through entrepreneurship development. The present research aims to identify the role of government policies on the relationship between innovations, technology and entrepreneurship development in Nigeria. Questionnaire was distributed to Small and Medium Scale Enterprises (SMEs) in Osun State. Structural Equation Model was used to analyze data using Partial Least Square Method (PLS 3). The results based on findings shows that a positive significant effect was found between government support, innovation and technology on entrepreneurship development. Government policies were found to have fully moderate the relationship between innovation, technology and entrepreneurship development. Government polices was found to be the most explanatory variable of the study having the highest effect on the entrepreneurship development. Therefore, the study recommends that government should enact policies that will encourage and promote the use of technology and support innovative ideas to bring economic growth through entrepreneurship development.

The partial least square Structural Equation modeling is carried out in SMART PLS 3 software. The formulated structural equation model is given in the figure 1. Bootstrapping with complete bootstrapping option is carried out to find standard error and the T-values. Comparing the T-Value with 95% percentage confidence value of 1.96, the above hypotheses are tested.

Abstract. In the last few years, football betting had known a large ex- pansion in the world, using different ways to try to guess and predict the unknown in the sport. Every time, people try to prognosticate the results of matches using probabilistic, statistic and other methods to get the maximum benefits, especially with the emerging of betting web- sites. In this paper, we present an alternative approach, to state of the art probabilistic models, based on Partial Least Square Path Modeling (PLS-PM). We first show that the simple PLS model containing only statistical resources about each team are efficient to predict the team ranking at d+1 and this gives a state of the art prediction of match outcomes. We then take advantage of PLS ability of integrating complex and heterogeneous data to reach a practical model by including textual data, taken from tweets related to teams, that we previously classify by polarity using robust sentiment analysis in multiple languages. Another learning of our experiment is the role of the inner model in PLS when used for prediction purpose. Unlikely Bayesian networks, the latent vari- able used in the prediction need to be deeply inside the inner model and not considered as marginal outcomes, this to allow back and forth retro-propagation from multiple types of data. The main purpose of our work is to show that PLS-PM can be surprisingly efficient in predicting tournament outcomes for which temporal statistics and social network data are available if inference is based on central inner latent variables.

Figure 1 shows the smoothed curve of the negative loga- rithm of the significances in the single marker analyses. QTL for the continuous trait were located on chromo- somes 1 and 3 with smaller QTL on all chromosomes. The effects of QTL for the discrete trait were smaller compared to the the continuous trait with QTL on chromosomes 1, 2 and 3. Figure 1 suggests at least three pleiotropic QTL; one at approximately half the length of chromosome 1, one at the beginning of chromosome 3 and another at approximately 0.25 the length of chro- mosome 4.

At the level of the structural model, the path coefficients were evaluated first in terms of sign and significance. They reflect the standardized beta coefficients for which asymptotic t-statistics were computed from the boot- strapping procedure. Second, the determination coeffi- cient R 2 – analogous to multiple regression – reflects the level or share of the composites’ explained variance. It was analysed for the endogenous composites. Third, the effect size f 2 was computed to determine whether an exogenous construct substantially influenced an en- dogenous construct. Similar to the traditional partial F- test, the change in R 2 is computed if the respective ex- ogenous construct is omitted. Fourth, the predictive relevance of the structural model was evaluated to deter- mine how well the model parameters can be recon- structed using the model and the PLS parameters. For this purpose, the blindfolding procedure was performed in SmartPLS, which calculates Stone–Geisser’s Q 2