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In this paper, a mixture of exponential and Rayleigh distributions in the proportions α and 1 − α and all the parameters in the mixture distribution are estimated based on fuzzy data.

The methods such as maximum likelihood estimation (MLE) and method of moments (MOM) are applied for estimation. Fuzzy data of triangular fuzzy numbers and Gaussian fuzzy numbers for different sample sizes are considered to illustrate the resulting estimation and to compare these methods. In addition to this, the obtained results are compared with existing results for crisp data in the literature.

The application of fuzziness in the data will be very useful to obtain precise results in the presence of vagueness in data. Mean square errors (MSEs) of the resulting estimators are computed using crisp data and fuzzy data. On comparison, in terms of MSEs, it is observed that maximum likelihood estimators perform better than moment estimators.

Classical methods of obtaining estimators of unknown parameters fail to give realistic estimators since these methods assume the data collected to be crisp or exact. Normally, such case of precise data is not always feasible and realistic in practice. Most of them will be incomplete and sometimes expressed in linguistic variables. Such data can be handled by generalizing the classical inference methods using fuzzy set theory.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

When the probability of each model is known, a natural idea is to select the most probable model. However, in many practical situations, the exact values of these probabilities are not known; only the intervals that contain these values are known. In such situations, a natural idea is to select some probabilities from these intervals and to select a model with the largest selected probabilities. The purpose of this study is to decide how to most adequately select these probabilities.

It is desirable to have a probability-selection method that preserves independence. If, according to the probability intervals, the two events were independent, then the selection of probabilities within the intervals should preserve this independence.

The paper describes all techniques for decision making under interval uncertainty about probabilities that are consistent with independence. It is proved that these techniques form a 1-parametric family, a family that has already been successfully used in such decision problems.

This study provides a theoretical explanation of an empirically successful technique for decision-making under interval uncertainty about probabilities. This explanation is based on the natural idea that the method for selecting probabilities from the corresponding intervals should preserve independence.

In Brazil, over 4.7 million women enrolled in university in the year 2017. However, Brazilian women have been consistently overrepresented in humanities and care majors and underrepresented in science, technology, engineering and mathematics (STEM). Given that observed gender differences in math-intensive fields have lasting effects on gender inequality in the labor market, and that observed gender variations do not necessarily associate with differences in innate ability, in this paper we explore the paths of societal gender bias and gender differences in a Brazilian university.

We conduct a social experiment at a University in Southeastern Brazil, applying the gender-STEM Implicit Association Test.

We found that women in STEM are less likely to show gender-STEM implicit stereotypes, compared to women in humanities. The results indicate a negative correlation between implicit gender stereotyping and the choice of math-intensive majors by women.

The stereotype-congruent results are indicative of the gender bias in Brazilian society, and suggest that stereotypes created at early stages in life are directly related to future outcomes that reinforce gender disparities in Brazil, which can be observed in career choices.

This study aims to investigate two issues: (1) a nexus between climate-related financial policies (CRFP) and global value chains (GVC) and (2) the government’s policies to help countries enhance the efficient use of CRFP in improving a country’s likelihood to participate in GVC.

To investigate the connection between GVC and CRFP, the authors incorporate that backward participation is measured using foreign value-added, while domestic value-added is used to measure forward participation, quantified as proportions of gross exports. The study analyses yield significant insights across a span of 20 developing countries and 26 developed countries over the period from 2010 to 2020.

Regarding the first issue, the authors affirm the presence of a linear link between GVC and CRFP, implying that involvement in CRFP is advantageous for both backward and forward participation. Furthermore, the authors identify long-term GVC and CRFP cointegration and confirm its long-term effects. Notably, the expression of a linear relationship between GVC and CRFP appears to be stronger in developing countries.

The study findings, together with previous research, highlight the importance of financial policies relating to climate change (CRFP) in the context of economic growth. Climate change’s consequences for financial stability and GVC highlight the importance of expanded policymakers and industry participation in tackling environmental concerns.

Regarding the second issue, the study findings suggest critical policy implications for authorities by highlighting the importance of financial stability and expanded policymakers in promoting countries' participation in GVC.

This paper investigates the link between GVC performance and CRFP, offering three significant advances to previous research. Moreover, as a rigorous analytical method, this study adopts a typical error model with panel correction that accounts for cross-sectional dependency and stationarity.

By older adulthood, the majority of individuals will have experienced at least one traumatic event. Trauma-informed care (TIC) is proposed to improve effectivity of health-care provision and to reduce likelihood of services causing retraumatisation. This study aims to assess the effectiveness of staff training in TIC in older adult services.

TIC training was delivered across eight Older Adult Community Mental Health Teams in the same UK organisation. Questionnaires were administered before and after training: a psychometrically robust measure, the Attitudes Related to Trauma-Informed Care, was used to assess TIC-related attitudes, and a service-developed scale was used to measure changes in TIC competence. Data was analysed using linear mixed effects modelling (LMM). Qualitative data regarding the impact of training was gathered one month after training through a free-text questionnaire.

There were 45 participants, all of whom were white British. LMM on pre- and post-data revealed that staff training significantly increased competencies across all measured TIC domains. Overall, staff attitudes were also significantly more trauma-informed after training. Qualitatively, staff identified time as the only additional resource required to deliver the skills and knowledge gained from training.

Training was found to be effective in increasing TIC-related skills and attitudes. Organisations aiming to become trauma-informed should consider staff training as one aspect of a wider development plan.

To the best of the authors’ knowledge, this paper is the first to examine TIC training for staff working in Older Adults Mental Health Services. Recommendations for services aiming to develop a trauma-informed culture have been provided.

This study investigated consumers’ willingness to pay (WTP) for a local, organic chicken breed “Taphao Thong-Kasetsart” and the drivers that shape consumers’ WTP across different meat preferences in Thailand.

Face-to-face interviewing was used to collect data at food-service outlets in Bangkok and Nakhon Pathom provinces, Thailand. Data analysis used the double-bounded dichotomous choice model across different consumers’ meat preferences for preferred chicken and non-preferred chicken consumers.

The results showed that there were different WTP amounts for local organic chicken across consumers’ meat preferences, with a higher WTP among non-preferred chicken consumers. This indicated that local organic chicken may attract more consumers in the alternative market. Consumers’ values and attitudes to taste drove chicken-preferred consumers to pay a premium for local organic chicken; health concerns mattered for non-preferred chicken consumers. These findings should provide useful information for food marketing campaigns based on consumers’ preferences.

The study contributed to understanding consumer heterogeneous preferences toward WTP for local organic chicken. The findings indicated that analyzing WTP across different meat preferences highlighted more effective marketing strategies to achieve the premium that consumers would pay. These strategies could help farmers to enlarge their local organic market share, leading to increased revenue and farmers’ well-being.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

This study aims to determine white wine drinkers’ interest in Grüner Veltliner wine, a potential signature wine for the Commonwealth of Pennsylvania in the USA and identify the consumer segments likely to look for and purchase this wine varietal.

A total of 676 wine consumers from the Mid-Atlantic region in the USA were compared based on familiarity with Grüner Veltliner wine, variety-seeking (VARSEEK) scores and the likelihood of looking for and purchasing Pennsylvania Grüner Veltliner wine.

Although only a third of participants had some experience with Grüner Veltliner wine, 77% were “somewhat interested” to “very interested” in being able to sample and taste the wine, and 66%–67% were “somewhat likely” to “very likely” to look for and purchase the wine from both growing regions. Generation, wine consumption behavior and familiarity with Pennsylvania wine and Grüner Veltliner wine differed between participants based on purchase intent and VARSEEK scores (i.e. low VARSEEK/likely, high VARSEEK/unlikely). Differences in factors motivating Grüner Veltliner purchasing were also identified between low VARSEEK/likely and high VARSEEK/likely participants.

The paper presents evidence of potential demand for a signature wine from an emerging wine region. This provides direction for target marketing and related promotional strategy, along with identifying wine consumers interested in tasting and purchasing the wine.

The purpose of the study is to estimate the willingness to pay (WTP) for major chemical fertilizers and revisit the fertilizer subsidy policy in Nepal.

We surveyed 619 households from six districts and assessed farmers’ WTP for urea, diammonium phosphate (DAP) and muriate of potash (MOP) during the fertilizer crisis. Our study elicited the WTP for fertilizers when fertilizers were not available on the market. A modified payment card approach was used to elicit farmers’ WTP.

The study found that farmers who buy fertilizer from agrodealers, buy from gray markets, have bank accounts, are willing to take a risk, have strong or medium economic conditions and incur higher travel costs have a higher WTP for fertilizers. Farmers in sampled areas, on average, are willing to pay 31 percent more for urea, 13 percent more for DAP and 19 percent more for MOP than the government recommended fertilizer price.

The design of the payment card and the estimation techniques used to fit the valuation function are likely to influence WTP.

Overall, literature on households’ WTP for fertilizers in developing countries is scarce. Our study contributes to the knowledge of WTP for fertilizers.