Bootstrap Approach Research Articles

Is ChatGPT the right technology for service individualization and value co-creation? evidence from the travel industry

ABSTRACT This paper aims to outline the influences of ChatGPT on service individualization and service value co-creation in the travel industry. Data were collected from managers and experts of travel agencies in Turkey. Hypotheses were tested via the independent sample t-tests, enter regression analysis, and the bootstrapping approach. It is found that ChatGPT empowered service individualization and internalizing information, which in turn positively impacts service value co-creation. The study also finds that ChatGPT has a significant moderating impact on the relationship between internalizing information and service individualization as well as on the relationship between service individualization and service value co-creation.

CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING

Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel.

Fake Z

Recently introduced connections between quantum codes and Narain CFTs provide a simple ansatz to express a modular-invariant function Zleft(tau, overline{tau}right) in terms of a multivariate polynomial satisfying certain additional properties. These properties include algebraic identities, which ensure modular invariance of Zleft(tau, overline{tau}right) , and positivity and integrality of coefficients, which imply positivity and integrality of the \U0001d532(1)n × \U0001d532(1)n character expansion of Zleft(tau, overline{tau}right) . Such polynomials come naturally from codes, in the sense that each code of a certain type gives rise to the so-called enumerator polynomial, which automatically satisfies all necessary properties, while the resulting Zleft(tau, overline{tau}right) is the partition function of the code CFT — the Narain theory unambiguously constructed from the code. Yet there are also “fake” polynomials satisfying all necessary properties, that are not associated with any code. They lead to Zleft(tau, overline{tau}right) satisfying all modular bootstrap constraints (modular invariance and positivity and integrality of character expansion), but whether they are partition functions of any actual CFT is unclear. We consider the group of the six simplest fake polynomials and denounce the corresponding Z’s as fake: we show that none of them is the torus partition function of any Narain theory. Moreover, four of them are not partition functions of any unitary 2d CFT; our analysis for other two is inconclusive. Our findings point to an obvious limitation of the modular bootstrap approach: not every solution of the full set of torus modular bootstrap constraints is due to an actual CFT. In the paper we consider six simple examples, keeping in mind that thousands more can be constructed.

Confidence Intervals for Mean and Difference between Means of Delta-Lognormal Distributions Based on Left-Censored Data

A delta-lognormal distribution consists of zero and positive values. The positive values follow a lognormal distribution, which is an asymmetric distribution. It is well known that the logarithm of these values follows a normal distribution, which is a symmetric distribution. The delta-lognormal distribution is used in medical and environmental sciences. This study considers the challenges of constructing confidence intervals for the mean and difference between means of delta-lognormal distributions containing left-censored data and applies them to compare two daily rainfall average areas in Thailand. Three different approaches for constructing confidence intervals for the mean of the delta-lognormal distribution containing left-censored data, based on the generalized confidence interval approach, the Bayesian approach, and the parametric bootstrap approach, are developed. Moreover, four different approaches for constructing confidence intervals for the difference between means of delta-lognormal distributions containing left-censored data, based on the generalized confidence interval approach, the Bayesian approach, the parametric bootstrap approach, and the method of variance estimates recovery approach, are considered. The performance of the proposed confidence intervals is evaluated by Monte Carlo simulation. The simulation studies indicate that the Bayesian approach can be considered as an alternative to construct a credible interval for the mean of the delta-lognormal distribution containing left-censored data. Additionally, the generalized confidence interval and Bayesian approaches can be recommended as alternatives to estimate the confidence interval for the difference between means of delta-lognormal distributions containing left-censored data. All approaches are illustrated using the daily rainfall data from Chiang Mai and Lampang provinces in Thailand.

0147 Does Sleep Regularity Link Child Maltreatment to Depressive Symptoms among Incoming First-Year College Students?

Abstract Introduction Sleep patterns may partially account for associations between early life adversity and poor health outcomes; however, few studies have empirically tested this hypothesis. We examined whether an index of sleep regularity mediated associations between child maltreatment (CM) and depression symptoms among emerging adults undergoing the major life transition of starting college. Methods First-year college students (N=731; 41% male; 48% Non-Hispanic White, 21% Non-Hispanic Asian, 15% Hispanic all races, 5% Non-Hispanic Black, and 11% Non-Hispanic other races) completed daily sleep diaries (DSDs) for 9 weeks and completed the Childhood Trauma Questionnaire (CTQ) and Center for Epidemiologic Studies Depression Scale (CES-D) following DSD completion. DSD data were used to compute participants’ Sleep Regularity Index (SRI). We used a nonparametric SEM bootstrap approach to examine whether sleep regularity mediated associations between childhood trauma and depressive symptoms, controlling for sex, race/ethnicity, and US vs. international status. Separate models were run for CTQ total score and five CTQ subscale scores: emotional, physical, and sexual abuse, and physical and emotional neglect. Results The prevalence of moderate to severe CM was 20.7%, and 40% reported clinically significant depressive symptoms at the end of the assessment period. Significant indirect effects of CTQ total score (estimate=0.02, 95% CI=0.003, 0.036); emotional abuse (0.06 [0.004, 0.107]); physical abuse (0.08 [0.003, 0.164]); sexual abuse (0.06 [0.014, 0.114]); emotional neglect (0.04 [0.002, 0.075]) on greater depression symptoms through lower sleep regularity were observed; and we saw no significant indirect effect of physical neglect (0.04 [-0.015, 0.089]). The percentage of the total effect of CM (i.e., CTQ total score and subscale scores) on greater depressive symptoms accounted for by lower sleep regularity were CTQ total score: 6%; physical abuse: 24%; sexual abuse: 19%; emotional abuse: 7%; and emotional neglect: 5%. Conclusion Variable sleep patterns account, in part, for associations between CM and depressive symptoms among first-year college students, with the largest effects observed for physical and sexual abuse. Sleep regularity should be included as a target in student health interventions on college campuses and may help buffer against poor mental health outcomes for students with CM. Support (if any) P20GM139743, MH079179, T32HD101392, P20GM139767.

Uncertainty in systemic risks rankings: Bayesian and frequentist analysis

We propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of systemic risk measures, LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest US financial institutions over the period of 2000–2020. The advantage of the Hamiltonian method is an efficient estimation of all parameters jointly in high dimensional models and providing posterior distributions incorporating parameter uncertainty. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric volatility. We estimate the systemic risks posterior distributions and two-step maximum likelihood distributions with bootstrap simulations for LRMES. The systemic risk rankings at different quantiles of the distributions vary considerably using bootstrap approach for computation of LRMES and SRISK, and are more stable with Bayesian posterior distributions using a parametric model. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on risk preferences with higher quantiles being more conservative.

Bootstrap inference for skew-normal unbalanced heteroscedastic one-way classification random effects model

In this paper, the one-sided hypothesis testing and interval estimation problems for the fixed effect and variance component functions are considered in the skew-normal unbalanced heteroscedastic one-way classification random effects model. Firstly, the Bootstrap approach is used to establish test statistic for fixed effect. Secondly, the test statistics and confidence intervals for variance component functions are constructed by Bootstrap approach and generalized approach, and their theoretical properties are discussed. The Monte Carlo simulation results indicate that the Bootstrap approach performs better than the generalized approach in most cases. Finally, the above approaches are illustrated with two real examples of the annual average concentrations of fine particulate matter and nitrogen dioxide.

Semidefinite programming algorithm for the quantum mechanical bootstrap.

We present a semidefinite programming algorithm to find eigenvalues of Schrödinger operators within the bootstrap approach to quantum mechanics. The bootstrap approach involves two ingredients: a nonlinear set of constraints on the variables (expectation values of operators in an energy eigenstate), plus positivity constraints (unitarity) that need to be satisfied. By fixing the energy we linearize all the constraints and show that the feasibility problem can be presented as an optimization problem for the variables that are not fixed by the constraints and one additional slack variable that measures the failure of positivity. To illustrate the method we are able to obtain high-precision, sharp bounds on eigenenergies for arbitrary confining polynomial potentials in one dimension.

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data.

Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Induced seismicity has been a serious problem for many coal mines in the Upper Silesian Coal Basin in Poland for many decades. The occurring mining tremors of the rock mass generate seismic vibrations that cause concern to the local population and in some rare cases lead to partial damage to buildings on the surface. The estimation of peak ground acceleration values caused by high energy mining seismic tremors is an important part of seismic hazard assessment in mining areas. A specially designed bootstrapping procedure has been applied to estimate the ground motion prediction model and makes it possible to calculate the confidence intervals of these peak ground acceleration values with no assumptions about the statistical distribution of the recorded seismic data. Monte Carlo sampling with the replacement for 132 seismic records measured for mining seismic tremors exceeding 150 mm/s2 have been performed to estimate the mean peak ground acceleration values and the corresponding upper limits of 95% confidence intervals. The specially designed bootstrap procedure and obtained ground motion prediction model reflect much better the observed PGA values and therefore provide more accurate PGA estimators compared to the GMPE model from multiple regression analysis. The bootstrap analysis of recorded peak ground acceleration values of high-energy mining tremors provides significant information on the level of seismic hazard on the surface infrastructure. A new tool has been proposed that allows for more reliable determination of PGA estimators and identification in the areas in coal mines that are prone to high-energy seismic activity.

The impact of value co-creation orientation on radical service innovation: Exploring a serial mediation mechanism

By integrating the dynamic capability perspective and service-dominant logic (SDL), this study empirically investigates the distinct roles of internal and external knowledge integration capabilities in the relationship between value co-creation orientation and radical service innovation. Based on survey data from a sample of 200 service innovation projects from IT and Internet-related industries in China, this study uses structural equation modeling (SEM) and a bootstrapping approach to test the hypothesized relationships in the conceptual model. The results show that external knowledge integration capability mediates the relationship between value co-creation orientation and radical service innovation. In addition, internal and external knowledge integration capability jointly play a serial mediating role between value co-creation orientation and radical service innovation. These findings contribute to the growing literature on value co-creation in radical service innovation, and offer service managers meaningful implications that reveal how to convert multiple sources of knowledge resources into superior radical service innovation.

Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach

Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach

First look at the evaluation of three-loop non-planar Feynman diagrams for Higgs plus jet production

We present new computations for Feynman integrals relevant to Higgs plus jet production at three loops, including first results for a non-planar class of integrals. The results are expressed in terms of generalised polylogarithms up to transcendental weight six. We also provide the full canonical differential equations, which allows us to make structural observations on the answer. In particular, we find a counterexample to previously conjectured adjacency relations, for a planar integral of the tennis-court type. Additionally, for a non-planar triple ladder diagram, we find two novel alphabet letters. This information may be useful for future bootstrap approaches.

Bridging positivity and S-matrix bootstrap bounds

The main objective of this work is to isolate Effective Field Theory scattering amplitudes in the space of non-perturbative two-to-two amplitudes, using the S-matrix Bootstrap. We do so by introducing the notion of Effective Field Theory cutoff in the S-matrix Bootstrap approach. We introduce a number of novel numerical techniques and improvements both for the primal and the linearized dual approach. We perform a detailed comparison of the full unitarity bounds with those obtained using positivity and linearized unitarity. Moreover, we discuss the notion of Spin-Zero and UV dominance along the boundary of the allowed amplitude space by introducing suitable observables. Finally, we show that this construction also leads to novel bounds on operators of dimension less than or equal to six.

Evaluating the Bantu parametric grammar in grammatical framework using Swahili grammar

Monologue grammar development for under-resourced languages is very slow and laborious (involves creating rules to generate the computational grammar to enable analysis and synthesis of the language(s) in question). However, the need for computational grammar continues to soar in this technology-driven economy for information synthesis and analysis. This paper aims to set up an experiment in the grammatical framework (GF), to evaluate the efficiency and effectiveness of the Bantu parameterized grammar to bootstrap a new grammar for Swahili. The goal is to investigate how this approach of bootstrapping grammar in a multilingual environment is effective and efficient in reducing the development effort. The bootstrapping approach uses the GF morphology-driven approach to develop portable and unique segments of Swahili grammar. The bootstrapped Swahili grammar resulted in a shareability of 100%, 71.11%, 68.75%, and 91.41% at category linearization, paradigms, parameters and syntax rules respectively. The portability was at 15.55%, 18.57%, and 8.59% at paradigms, parameters and syntax rules, respectively. Finally, this paper contributes in: first, provides an approach that leads to an effective and efficient method for developing and bootstrapping computational grammar for the under-resourced Bantu languages. Secondly, the research provided a Swahili grammar.

The Nexus Between High-Involvement Work Practices and Employees' Proactive Behavior in Public Service Organizations: A Time-Lagged Moderated-Mediation Model.

Public service organizations may improve the quality of services they offer citizens by instilling proactive behavior in their employees. This study aimed to provide insights on how high-involvement work practices may indirectly facilitate proactive behavior in frontline government employees via employee commitment. A time-lagged approach was used to collect data from 542 frontline employees in three waves at 3-week intervals. We tested the hypothesized moderated mediation model using a PROCESS macro bootstrap approach. A moderated-meditation model was applied in which public service motivation was theorized to increase the mediating effect of employee commitment on the relationship between high-involvement work practices and employee proactive behavior. As predicted, the findings show that supervisor' deviant behavior attenuated the mediating effect of employee commitment on the relationship between high-involvement work practices and employee proactive behavior. The findings of this research contribute to the emerging literature on public management and have implications for public sector organizations seeking to improve the quality of services they offer citizens.

Coping with imbalanced data problem in digital mapping of soil classes

AbstractAn unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over‐ and under‐sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost‐sensitive learning and one‐class classification (OCC) of the minority class combined with multi‐class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost‐sensitive decision tree (CSDT) within the C5.0 algorithm, and a one‐class support vector machine combined with multi‐class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold‐out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study approach demonstrates that the three newly suggested methods can remarkably increase both overall and individual class accuracy for mapping.

MULTIFACETED FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: BOOTSTRAP PANEL CAUSALITY APPROACH

A large body of both theoretical and empirical literature has been conducted to show the relationship between financial development (or called financial depth) and economic growth. However, for Central Europe and Baltic countries, the existing empirical evidence is scarce. This paper revisits whether financial development stimulates economic growth in the context of eleven Central Europe and Baltic countries during the 2000-2019 period. By incorporating broad characteristics of financial institutions (such as banks and insurance companies) and financial markets (including stock markets, bond markets, and derivative markets) into the finance-growth nexus, this paper not only attempts to underline the potential importance of financial depth for economic growth but also tests the hypothesis that financial access, efficiency, and stability promote economic growth or vice versa. This paper applies a panel bootstrap approach to Granger causality testing, which considers cross-sectional dependence and heterogeneity issues. Empirical results show limited support for the supply-leading and demand following hypotheses.

The effect of crop insurance in Ecuadorian rice farming: a technical efficiency approach

PurposeThe aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.Design/methodology/approachThe authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.FindingsMost uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.Social implicationsThe results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.Originality/valueThis paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.

An Improvement in Maximum Likelihood Estimation of the Gompertz Distribution Parameters

In this study, we will look at estimating the parameters of the Gompertz distribution. We know that the maximum likelihood technique is the most often used method in the literature for parameter estimation. However, it is well known that the maximum likelihood estimators (MLEs) are biased for small sample sizes. As a result, we are motivated to produce nearly unbiased estimators for the parameters of this distribution. To be more specific, we concentrate on two bias-correction strategies (analytical and bootstrap approaches) to minimize MLE biases to the second order of magnitude. Monte Carlo simulations are used to compare the performances of these estimators. Finally, two real-data examples are offered to demonstrate the utility of our proposed estimators in small sample sizes.