Inspecting Hybrid Data Mining Approaches in Decision Support Systems for Humanities Texts Criticism

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automatically-supported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content. When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniques in humanities when summarizing and eliciting automated decisions. This process relies on technological advancement and considers (1) the automated-decision support-techniques commonly used in humanities, (2) the performance evolution and the use of the stylometric approach in text-mining, and (3) the comparisons of the results of chunking text by using different attributes in Burrows' Delta method. This study also provides an overview of the efficiency of applying some selected data-mining (DM) methods with various text-mining techniques to support the critics' decision in artistry ‒ one field of humanities. The automatic choice of criticism in this field was supported by a hybrid approach to these procedures.

Estimation of Stress-Strength Reliability for Multicomponent System with Rayleigh Data

Inference is investigated for a multicomponent stress-strength reliability (MSR) under Type-II censoring when the latent failure times follow two-parameter Rayleigh distribution. With a context that the lifetimes of the strength and stress variables have common location parameters, maximum likelihood estimator of MSR along with the existence and uniqueness is established. The associated approximate confidence interval is provided via the asymptotic distribution theory and delta method. Meanwhile, alternative generalized pivotal quantities-based point and confidence interval estimators are also constructed for MSR. More generally, when the lifetimes of strength and stress variables follow Rayleigh distributions with unequal location parameters, likelihood and generalized pivotal-based estimators are provided for MSR as well. In addition, to compare the equivalence of different strength and stress parameters, a likelihood ratio test is provided. Finally, simulation studies and a real data example are presented for illustration.

Moderated mediation and J-curve emergence in path models: an information systems research perspective

Purpose J-curve relationship analyses can provide valuable insights to information systems (IS) researchers. This paper aims to discuss moderated mediation in IS research and the related emergence of J-curve relationships. Design/methodology/approach Building on an illustrative study in the field of IS, the author Lays out three steps to combine moderation and J-curve analyses, with the goal of more fully understanding the underlying moderated mediation relationships. The paper proposes a new segmentation delta method to test for J-curve emergence, as part of this framework. Findings The paper shows, in the context of this study, the complementarity of moderation and J-curve analyses. Research limitations/implications Currently, IS researchers rarely conduct moderation and J-curve analyses in a complementary way, even though there are software tools, and related methods, which allow them to do so in a relatively straightforward way. Originality/value The analyses were conducted with the software WarpPLS, a widely used tool that allows for moderated mediation and J-curve analyses, in a way that is fully compatible with the set of steps presented in this paper.

Estimating the natural indirect effect and the mediation proportion via the product method

Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package is developed to implement the methods for point and variance estimation discussed in this paper.

Climate Change, Farming, and Gardening in Alaska: Cultivating Opportunities

Ongoing climate change and associated food security concerns are pressing issues globally, and are of particular concern in the far north where warming is accelerated and markets are remote. The objective of this research was to model current and projected climate conditions pertinent to gardeners and farmers in Alaska. Research commenced with information-sharing between local agriculturalists and climate modelers to determine primary questions, available data, and effective strategies. Four variables were selected: summer season length, growing degree days, temperature of the coldest winter day, and plant hardiness zone. In addition, peonies were selected as a case study. Each variable was modeled using regional projected climate data downscaled using the delta method, followed by extraction of key variables (e.g., mean coldest winter day for a given decade). An online interface was developed to allow diverse users to access, manipulate, view, download, and understand the data. Interpretive text and a summary of the case study explained all of the methods and outcomes. The results showed marked projected increases in summer season length and growing degree days coupled with seasonal shifts and warmer winter temperatures, suggesting that agriculture in Alaska is undergoing and will continue to undergo profound change. This presents opportunities and challenges for farmers and gardeners.

Extensions and Complements

This chapter contains treatments of a range of topics associated with the central limit theorem. These include estimated normalization using methods of heteroscedasticity and autocorrelation consistent variance estimation, the CLT in linear prrocesses, random norming giving rise to a mixed Gaussian limiting distribution, and the Cramér–Wold device and multivariate CLT. The delta method to derive the limit distributions of differentiable functions is described. The law of the iterated logarithm is proved for Gaussian processes.

Do Early-Life Social, Behavioral, and Health Exposures Increase Later-Life Arthritis Incidence?

Objectives: This study investigates direct and indirect influences of childhood social, behavioral, and health exposures on later-life osteoarthritis and rheumatoid arthritis development. Methods: Drawing from cumulative inequality theory and six waves of the Health and Retirement Study (2004–2014), we estimate structural equation modeling-based discrete-time survival analysis of the association between six childhood exposure domains and both osteoarthritis and rheumatoid arthritis incidence for men ( n = 2720) and women ( n = 2974). Using the delta method to test for mediation, we examine indirect effects via selected health-related risks and resources. Results: Risky adolescent behavior is associated with rheumatoid arthritis incidence for women (h.O.R. = 1.883, 95% C.I. [1.016, 3.490]), whereas several types of childhood exposures are associated with later-life osteoarthritis development for both men and women. Experiencing two or more childhood socioeconomic disadvantages is indirectly associated with osteoarthritis (men: coef. = 0.024, 95% C.I. [0.003, 0.045]; women: coef. = 0.111, 95% C.I. [0.071, 0.150]) and rheumatoid arthritis (men: coef. = 0.037, 95% C.I. [0.000, 0.074]; women: coef. = 0.097, 95% C.I. [0.035, 0.159]) development through adult body mass index. Discussion: Findings highlight the importance of childhood contexts in understanding the development of later-life osteoarthritis and rheumatoid arthritis.

Evaluating the Applicability of a Quantile–Quantile Adjustment Approach for Downscaling Monthly GCM Projections to Site Scale over the Qinghai-Tibet Plateau

In the context of global climate change, the Qinghai-Tibetan plateau (QTP) has experienced unprecedented changes in its local climate. While general circulation models (GCM) are able to forecast global-scale future climate change trends, further work needs to be done to develop techniques to apply GCM-predicted trends at site scale to facilitate local ecohydrological response studies. Given the QTP’s unique altitude-controlled climate pattern, the applicability of the quantile–quantile (Q-Q) adjustment approach for this purpose remains largely unknown and warrants investigation. In this study, this approach was evaluated at 36 sites to ensure the results are representative of different climatic and surface conditions on the QTP. Considering the practical needs of QTP studies, the study aims to assess its capability for downscaling monthly GCM simulations of major variables onto the site scale, including precipitation, air temperature, wind speed, relative humidity, and air pressure, based on two GCMs. The calibrated projections at the sites were verified against the observations and compared with those from two commonly used adjustment methods—the quantile-mapping method and the delta method. The results show that the general trends of most variables considered are well adjusted at all sites, with a quantile pair of 25–75% for all the variables except precipitation where 10–90% is used. The calibrated results are generally close to the observed values, with the best performance in air pressure, followed by air temperature and relative humidity. The performance is relatively limited in adjusting wind speed and precipitation. The accuracies decline as the adjustment extends into the future; a wider adjustment window may help increase the performance for the variables subject to climate changes. It is found that the performance of the adjustment is generally independent of the locations and seasons, but is strongly determined by the quality of GCM simulations. The Q-Q adjustment works better for the meteorological variables with fewer fluctuations and daily extremes. Variables with more similarities in probability density functions between the observations and GCM simulations tend to perform better in adjustment. Generally, this approach outperforms the two peer methods with broader applicability and higher accuracies for most major variables.