Rival Models Research Articles

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practicalapplications.

MD-GCCF: Multi-view deep graph contrastive learning for collaborative filtering

Collaborative Filtering (CF), a classical recommender system approach, learns users’ interests and behavioral preferences for items through a user–item interaction graph. CF based on graph neural network (GNN) and CF based on graph contrastive learning (GCL) show strong advantages in both modeling multi-layer signals and solving label sparsity, respectively. However, there are still two key problems to be solved: Most CF models based on (1) GNN suffer from the over-smoothing problem and are unable to aggregate deep collaborative signals and (2) GCL adopts a single aggregation paradigm, resulting in a lack of diversity in the feature representation of collaborative signals. To solve the above problems, a multi-view deep graph contrastive learning for collaborative filtering (MD-GCCF) has been proposed from two perspectives. First, a deep graph collaborative signal aggregation module is proposed to learn potential intention similarity representations for deep collaborative signal propagation within a few layers. Second, a novel multi-view contrastive learning module has been proposed, utilizing both local and global contrastive learning views from the collaborative signal aggregation module to enhance deep structures and semantic features in collaborative signals. MD-GCCF improves by 9.52%, 3.34%, and 2.49% compared to the rival models, respectively, in the Amazon book, Yelp2018, and Gowalla datasets.

Model Robust Optimal Designs for Kronecker Model for Mixture Experiments

In comparison to Scheffè’s canonical polynomial models (S-models), the Kronecker models (K-models) for mixture experiments are symmetric, compact in notation, and based on the Kronecker algebra of vectors and matrices. Further, there is a corresponding transition from S-models to K-models in the form of model re-parameterization. In the literature, it has been recommended to use second-degree K-models in practice compared to the widely used second-degree S-models especially when the moment matrix is of an ill-conditioning type. The motivation of the present article is to discriminate between K-models and S-models in terms of the model-robust D- and A-optimality criteria. These optimality criteria are discussed when there is uncertainty in selecting an appropriate model out of two rival models for a mixture experiment. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 31-48

A study on big data analytics and innovation: From technological and business cycle perspectives

In today's rapidly changing business landscape, organizations increasingly invest in different technologies to enhance their innovation capabilities. Among the technological investment, a notable development is the applications of big data analytics (BDA), which plays a pivotal role in supporting firms' decision-making processes. Big data technologies are important factors that could help both exploratory and exploitative innovation, which could affect the efforts to combat climate change and ease the shift to green energy. However, studies that comprehensively examine BDA's impact on innovation capability and technological cycle remain scarce. This study therefore investigates the impact of BDA on innovation capability, technological cycle, and firm performance. It develops a conceptual model, validated using CB-SEM, through responses from 356 firms. It is found that both innovation capability and firm performance are significantly influenced by big data technology. This study highlights that BDA helps to address the pressing challenges of climate change mitigation and the transition to cleaner and more sustainable energy sources. However, our results are based on managerial perceptions in a single country. To enhance generalizability, future studies could employ a more objective approach and explore different contexts. Multidimensional constructs, moderating factors, and rival models could also be considered in future studies.

Modeling Partners’ Behavior in Long-Lasting B2B Supply Chain Relationships

Companies have strengthened their long-term inter-organizational partnerships throughout the supply chain to neutralize competitive pressures and risks in uncertain environments. On this basis, this research aims to propose and test a model of partners’ behavior aimed at the maintenance of long-term collaboration. By using confirmatory factor analysis, structural equation modeling, and rival model testing, the theoretical model proposed attempts to identify, from a seller’s perspective, the critical variables of partners’ behavior. It also seeks to understand the effect of satisfaction between trust and commitment (as antecedents associated with relationship quality) and sales formalization, sales opportunism, and sales-specific assets (as postcendents linked to relationship efficiency). Our findings verify the nomological framework and demonstrate that the partnership quality variables affect relationship efficiency, through sales satisfaction. However, the results of our research cannot confirm the relationship between satisfaction and specific assets. This research is relevant as it deals with inter-organizational partnerships from a seller-oriented approach, and it is based on a combination of Relationship Marketing Theory and Transaction Cost Theory to demonstrate that the inter-organizational partnership quality variables exert a direct effect on the partnership efficiency variables.

Religious Reasons and Liberal Legitimacy

Abstract This article addresses the exclusivism–inclusivism debate about religious reasons in law within a justificatory liberal framework. The question of whether religious reasons have justificatory capacity for attaining public justification has increasingly been seen as a matter of how public justification is understood between two rival models: the consensus model being aligned with exclusivism, the convergence model with inclusivism. More recently, however, that alignment has been challenged with attempts to show that consensus can reach an equivalent degree of inclusivism as convergence. Against this, I contend that the purported equivalence is misplaced. First, I identify a crucial ambiguity about public justification and two corresponding domains. Upon demonstrating these to be mutually independent and severable, I conclude that the moves to equalize the models are confined within the more narrow domain while, in the more fundamental domain, the choice of model continues to prove determinative as to the exclusivist or inclusivist valence of justificatory liberalism.

Power Topp–Leone exponential negative family of distributions with numerical illustrations to engineering and biological data

Abstract This article puts forth a novel category of probability distributions obtained from the Topp–Leone distribution, the inverse- K K exponential distribution, and the power functions. To obtain this new family, we used the original cumulative distribution functions. After introducing this new family, we gave the motivations that led us to this end and the basis of the new family obtained, followed by the mathematical properties related to the family. Then, we presented the statistic order, the quantile function, the series expansion, the moments, and the entropy (Shannon, Reiny, and Tsallis), and we estimated the parameters by the maximum likelihood method. Finally, using real data, we presented numerical results through data analysis with a comparison of rival models.

The odd log-logistic generalized exponential distribution: Application on survival times of chemotherapy patients data

Background The creation of developing new generalized classes of distributions has attracted applied and theoretical statisticians owing to their properties of flexibility. The development of generalized distribution aims to find distribution flexibility and suitability for available data. In this decade, most authors have developed classes of distributions that are new, to become valuable for applied researchers. Methods This study aims to develop the odd log-logistic generalized exponential distribution (OLLGED), one of the lifetime newly generated distributions in the field of statistics. The advantage of the newly generated distribution is the heavily tailed distributed lifetime data set. Most of the probabilistic properties are derived including generating functions, moments, and quantile and order statistics. Results Estimation of the model parameter is done by the maximum likelihood method. The performance of parametric estimation is studied through simulation. Application of OLLGED and its flexibilities is done using two data sets and while its performance is done on the randomly simulated data set. Conclusions The application and flexibility of the OLLGED are ensured through empirical observation using two sets of lifetime data, establishing that the proposed OLLGED can provide a better fit in comparison to existing rival models, such as odd generalized log-logistic, type-II generalized log-logistic, exponential distributions, odd exponential log-logistic, generalized exponential, and log-logistic.

Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering

The goal of this research is to create a new general family of Topp-Leone distributions called the Topp-Leone Cauchy Family (TLC), which is exceedingly versatile and results from a careful merging of the Topp-Leone and Cauchy distribution families. Some of the new family’s theoretical properties are investigated using specific results on stochastic functions, quantile functions and associated measures, generic moments, probability weighted moments, and Shannon entropy. A parametric statistical model is built from a specific member of the family. The maximum likelihood technique is used to estimate the model’s unknown parameters. Furthermore, to emphasize the new family’s practical potential, we applied our model to two real-world data sets and compared it to existing rival models.

Optimized Forecasting Model to Improve the Accuracy of Very Short-Term Wind Power Prediction

This article proposes a novel framework to improve the prediction accuracy of very short-term (5-min) wind power generation. The framework consists of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), monarch butterfly optimization (MBO) and long short-term memory (LSTM), called CEMOLS. The CEEMDAN is employed to extract complex hidden features of time-series data into intrinsic mode functions that are predicted using LSTM models with dropout regularization to retain long-term relationships between input and output data, while the optimization algorithm tunes the hyperparameters of the forecasting model. Data from four real wind farms in New South Wales are collected and preprocessed to train and test the forecasting models. Recently developed rival models are compared to identify the best-performing prediction model. The analysis demonstrates that the proposed CEMOLS with low computation time can improve forecasting accuracy on average by 32.96% in mean absolute error, 47.10% in root mean square error and 32.33% in mean absolute percentage error as compared to the benchmark Persistence model. It also demonstrates that sensitive and statistical analysis needs to be carried out to determine robust prediction models among rival models for practical application.

A multi-model approach for the extension of the use and gratification theory in video game streaming

PurposeVideo game streaming (VGS) is emerging as an extremely popular, highly interactive, inordinately subscribed and very dynamic form of digital media. Incorporated environmental elements, gratifications and user pre-existing attitudes in VGS, this paper presents the development of an extended model of uses and gratification theory (EUGT) for predicting users' behavior in novel technological context.Design/methodology/approachThe proposed model was empirically tested in VGS context due to its popularity, interactivity and relevance. Data collected from 308 VGS users and structural equation modeling (SEM) was employed to assess the hypotheses. Multi-model comparison technique was used to assess the explanatory power of EUGT.FindingsThe findings confirmed three significant types elements in determining VGS viewers' engagement, including gratifications (e.g. involvement), environmental cues (e.g. medium appeal) and user predispositions (e.g. pre-existing attitudes). The results revealed that emerging technologies provide potential opportunities for new motives and gratifications, and highlighted the significant of pre-existing attitudes as a mediator in the gratification-uses link.Originality/valueThis study is one of its kind in tackling the criticism on UGT of considering media users too rational or active. The study achieved this objective by considering environmental impacts on user behavior which is largely ignored in recent UGT studies. Also, by incorporating users pre-existing attitudes into UGT framework, this study conceptualized and empirically verified the higher explanatory power of EUGT through a novel multi-modal approach in VGS. Compared to other rival models, EUGS provides a more robust explanation of users' behavior. The findings contribute to the literature of UGT, VGS and users' engagement.

Revisiting Clarke and Stewart’s (1995) investigation of PM approval and vote intentions in the UK

Economic Evaluations, Prime Ministerial Approval and Governing Party Support: Rival Models Reconsidered by Harold D. Clarke and Marianne Stewart (1995) makes major contributions both substantive and methodological. Substantively, the article contributes to literatures on economic voting and the presidentialization of British politics. Methodologically, the paper is a model of how to conduct cointegration and error correction analyses between closely related political time series. In all, the authors established a long-run equilibrium between prime ministerial approval and vote intentions for the governing party. The two variables may diverge for short periods, but vote intentions will always revert back to a level that is in line with the popularity of the PM. We extend Clarke and Stewart’s data to the present day and examine how the PM-vote intention link has endured while the effects of the economy on vote intentions have varied. We also show that, while time series analysis in political science has undergone several revolutions, Clarke and Stewart’s process of conducting two-step cointegration analysis (Engle and Granger, 1987) stands up 30 years later as a blueprint for how to investigate long-run relationships with political data.

The odd log-logistic generalized exponential distribution: Application on survival times of chemotherapy patients data

Background: The creation of developing new generalized classes of distributions has attracted applied and theoretical statisticians owing to their properties of flexibility. The development of generalized distribution aims to find distribution flexibility and suitability for available data. In this decade, most authors have developed classes of distributions that are new, to become valuable for applied researchers. Methods: This study aims to develop the odd log-logistic generalized exponential distribution (OLLGED), one of the lifetime newly generated distributions in the field of statistics. The advantage of the newly generated distribution is the heavily tailed distributed lifetime data set. Most of the probabilistic properties are derived including generating functions, moments, and quantile and order statistics. Results: Estimation of the model parameter is done by the maximum likelihood method. The performance of parametric estimation is studied through simulation. Application of OLLGED and its flexibilities is done using two data sets and while its performance is done on the randomly simulated data set. Conclusions: The application and flexibility of the OLLGED are ensured through empirical observation using two sets of lifetime data, establishing that the proposed OLLGED can provide a better fit in comparison to existing rival models, such as odd generalized log-logistic, type-II generalized log-logistic, exponential distributions, odd exponential log-logistic, generalized exponential, and log-logistic.

Precursors and Outcomes of Perceived Value in B2B Banking Services: A Nomological Framework

This paper aims to test a nomological network of the precursors (perceived price, price fairness, and service quality) and the multi-dimensional outcomes (economic satisfaction and non-economic satisfaction; behavioral and attitudinal loyalty) of micro-enterprise customers’ perception of value. Structural equation modeling was used to test a research model and two rival models from data collected from 381 micro-enterprise banking customers. The results show that, by charging fair prices, banks will increase not only micro-enterprises’ positive perceptions of price and service quality, but also their perceptions of value. Positive value perceptions result in greater economic and non-economic satisfaction. This then results in loyalty in both the attitudinal and behavioral senses. Economic satisfaction should first be attained before micro-enterprise customers will be non-economically satisfied and show attitudinal loyalty before behavioral loyalty.

Translating imaging traits of mass-forming intrahepatic cholangiocarcinoma into the clinic: From prognostic to therapeutic insights

Background & AimsThe progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour underlying imaging traits. We developed and validated imaging-based prognostic systems for resected MICCs with an appraisal of the tumour immune microenvironment (TIME) underpinning patient-specific imaging traits. MethodsBetween January 2009 and December 2019, a total of 322 patients who underwent dynamic computed tomography/magnetic resonance imaging and curative-intent resection for MICC at three hepatobiliary institutions were retrospectively recruited, divided into training (n = 193) and validation (n = 129) datasets. Two radiological and clinical scoring (RACS) systems, one integrating preoperative variables and one integrating preoperative and postoperative variables, were developed using Cox regression analysis. We then prospectively analysed the TIME of tissue samples from 20 patients who met study criteria from January 2021 to December 2021 using multiplexed immunofluorescence. ResultsPreoperative and postoperative MICC-RACS systems built on carbohydrate antigen 19-9, albumin, tumour number, radiological/pathological nodal status, pathological necrosis, and three radiological traits (arterial enhancement pattern, tumour boundary, and capsular retraction) demonstrated good performance in predicting disease-specific (C-statistic >0.80) and disease-free (C-statistic >0.75) survival that outperformed rival models and staging systems across study cohorts (P <0.05 for all). Patients with MICC-RACS score of 0–2 (low risk), 3–5 (medium risk), and ≥6 (high risk) had incrementally worse prognosis after surgery. Significant differences in spatial distribution and infiltration level of immune cells were identified between arterial enhancement patterns. Enhanced infiltration of immunosuppressive regulatory T cells and M2-like macrophages at the invasive margin were noted in tumours with distinct boundary and capsular retraction, respectively. ConclusionsOur MICC-RACS systems are simple but powerful prognostic tools that may facilitate the understanding of spatially distinct TIMEs and patient-tailored immunotherapy approach. Impact and ImplicationsThe progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour of MICC underlying imaging traits. In this study, we proposed novel and easy-to-use tools, built on radiological and clinical features, that demonstrated good performance in predicting the prognosis either before or after surgery and outperformed rival models/systems across major imaging modalities. The characteristic radiological traits integrated into prognostic systems (arterial enhancement pattern, tumour boundary, and capsular retraction) were highly correlated with heterogeneous tumour-immune microenvironments, thereby renovating treatment paradigms for this difficult-to-treat disease.

Online Random Feature Forests for Learning in Varying Feature Spaces

In this paper, we propose a new online learning algorithm tailored for data streams described by varying feature spaces (VFS), wherein new features constantly emerge and old features may stop to be observed over various time spans. Our proposed algorithm, named Online Random Feature Forests for Feature space Variabilities (ORF3V), provides a strategy to respect such feature dynamics by generating, updating, pruning, as well as online re-weighing an ensemble of what we call feature forests, which are generated and updated based on a compressed and storage efficient representation for each observed feature. We benchmark our algorithm on 12 datasets, including one novel real-world dataset of government COVID-19 responses collected through a crowd-sensing program in Spain. The empirical results substantiate the viability and effectiveness of our ORF3V algorithm and its superior accuracy performance over the state-of-the-art rival models.

Antecedents of Retail Employees’ Service Performance: A Store-Level Exploration of a Climate-Centric versus an Engagement-Centric Approach

This paper compares two store-level models of the antecedents of frontline employees’ in-role and extra-role service performance in a retail setting. In the climate-centric research model, service climate serves as a direct antecedent of in-role and extra-role service performance, while in the engagement-centric rival model, work engagement directly predicts in-role and extra-role service performance. The two competing models were assessed at the store-level of analysis on data collected from 781 frontline employees and 70 managers. Results indicate that service-oriented high-performance work systems and work engagement both predict service climate which, in turn, predicts employees’ in-role and extra-role service performance.

A methodology to [formula omitted]-augment experimental designs

One of the main criticisms of optimal experimental designs is that they tend not to adequately meet the practical needs of the experimenter. For example, optimal designs for estimation of the parameters in a model frequently have too few designs points to check the model adequacy, to discriminate between rival models or to estimate a particular function of the parameters. Further, some experimenters like toxicologists have been schooled to using many doses in an animal experiment and there is great resistance to using a design with just a few doses. This paper uses the General Equivalence Theorem to define regions where the practitioner may select points flexibly to augment a design that meets the practical needs more adequately and has a user-specified minimum efficiency requirement. As examples, we demonstrate the usefulness of our theory-based method in various setups using statistical models, such as Antoine’s Equation, the Michaelis–Menten model and a quadratic heteroscedastic model. We also explore the power of the lack-of-fit test for several D-augmented designs in a simulation study. We provide user-friendly codes, either through the R package optedr or through a graphical user interface in Shiny, to facilitate practitioners implement our methodology to find an improved D-augmented designs for their problems.

The effects of augmented reality shopping experiences: immersion, presence and satisfaction

PurposeAugmented reality (AR) is transforming the business and interactive marketing landscape. This research aims to investigate consumers' degree of involvement and if a feeling of immersion and presence influences AR shopping satisfaction, comparing high- and low-immersive AR experiences.Design/methodology/approachThis paper uses a quantitative approach. Two studies were carried out: a high-immersive AR experiment with 173 participants and a low-immersive AR experience with 222 participants. Findings were analyzed using partial least square structural equation modeling with SmartPLS.FindingsResults indicate the antecedents of immersion and presence differ when it comes to different immersive AR levels. In a high-immersive AR experience, flow, information seeking and novelty are attributes related to immersion, while enjoyment and personalization are related to presence. Contrastingly, in a low-immersive AR experience, only flow is related to immersion, while information seeking, novelty and personalization are related to presence. These results highlight the role of immersion and presence as mediators for AR shopping satisfaction experience.Originality/valueThis study's originality lies in the use of a rival model for analysis. Findings suggest a contingent perspective of AR experience, depending on high- or low-immersion experience, so companies must pay attention for how to measure AR experiences to increase involvement and satisfaction.

Evaluation of Paleomagnetic Bias in Ediacaran Global Paleogeographic Reconstructions

AbstractPaleogeographic models provide first‐order insights into Earth's tectonic evolution in deep geologic time. However, there is a lack of objective metrics to distinguish between rival models. Using a recent compilation of reliable Precambrian paleomagnetic data, we quantify the extent of paleomagnetic compliance of existing Ediacaran global reconstruction models. We determine spherical arc distances between modeled paleopositions of the geographic South Pole and reconstructed coordinates of all paleopoles from a continent acquired at various times. We identify significant paleomagnetic bias for a reconstructed location of a continent if arc distances of the majority of high‐quality paleopoles with reliable age constraints are greater than their standard errors at the 95% confidence interval. Our study provides quantifiable measures of paleogeographic bias that allows researchers to make informed decisions when selecting among existing global models as a foundation for interpreting their geologic data.