Credible Estimates Research Articles

A Critical Assessment of Geological Weighing Lysimeters: Part 1—Monitoring Field Scale Soil Moisture Storage

ABSTRACTSoil moisture plays a critical role in the exchange and partitioning of water and energy fluxes at the land surface. Representative measurements of field scale (104–106 m2) soil moisture are needed to characterise hydrological processes, and to calibrate and constrain hydrology, weather and climate predictions. However, measuring soil moisture proves difficult as it varies spatially, as a function of heterogeneity of the land‐surface, and temporally, as a function of dynamic drivers of precipitation, snowmelt, and evapotranspiration. Conventional soil moisture measurement techniques provide point scale (< 1 m2) estimates, while remote sensing applications provide large‐scale estimates (> 106 m2). While both spatial scales are useful for certain applications, these methods must be upscaled or downscaled to estimate field‐scale soil moisture. Geological weighing lysimeters (GWL) provide a method to measure total water storage at field scale. This instrument utilises the relationship between hydraulic head and mechanical loading to quantify change in water mass on the lands surface. In this study, we examine a GWL's ability to estimate total water storage and to partition soil moisture storage using complementary observations of snow storage and shallow groundwater storage. We found the GWL record provided credible estimates of total water storage dynamics at our site. The GWL derived soil moisture captured the observed seasonal dynamics at the site. We interpreted the differences in GWL and point scale soil moisture as being due to (i) the dielectric probes inability to sense soil ice content in the winter; and (ii) sub‐field scale heterogeneity in the fluxes (evapotranspiration and drainage) in the summer and fall. We show that GWL storage estimates are credible, and when combined with supplementary storage observations, they can provide accurate estimates of soil moisture storage and valuable insights into the hydrological processes occurring at the site.

PURPOSIVE SAMPLING: A REVIEW AND GUIDELINES FOR QUANTITATIVE RESEARCH

Purposive sampling has been extensively utilized across research disciplines, particularly in business and management studies. While existing literature has primarily focused on its application in qualitative research, the rationale for using it in quantitative contexts remains underexplored, leaving researchers with limited practical guidance. Moreover, much of the available literature is found within academic texts, specifically books, which may not always provide sufficiently actionable guidance on successfully implementing and reporting purposive sampling in quantitative research. This editorial addresses these gaps by presenting structured guidelines specifically tailored for applying purposive sampling in quantitative research. It provides practical recommendations for systematically implementing and transparently reporting purposive sampling. The editorial discusses different types of purposive sampling, including criterion sampling, maximum variation sampling, and theoretical sampling, and illustrates their applications in quantitative contexts. It also discusses how purposive sampling can be combined with other techniques, such as snowball sampling and quota sampling, to enhance data quality and relevance. Detailed step-by-step guidelines are presented for employing purposive sampling and reporting its use in quantitative studies in a robust manner. Additionally, we put forward a salient checklist for authors and reviewers to ensure rigor, transparency, and consistency in reporting. This work contributes to the growing discourse about sampling by presenting one of the first comprehensive frameworks for employing purposive sampling in quantitative research. By guiding researchers to adopt purposive sampling with greater rigor and transparency, we hope it will be useful to establish the representativeness of data and provide more credible estimations to explain or predict the phenomena under investigations.

Interval Estimation for the Two-Parameter Exponential Distribution Based on Upper Record Value Data Using Bayesian Approaches

Using upper record value data, we provide a credible interval estimate for the scale parameter of a two-parameter exponential distribution based on Bayesian methods. Additionally, we propose two Bayesian credible confidence regions for both parameters. In addition to interval estimations for the parameters, we propose a Bayesian prediction interval for a future upper record value. A simulation study is conducted to compare the performance of the proposed Bayesian credible interval, regions and prediction intervals with existing non-Bayesian approaches, focusing on coverage probabilities. The simulation results show that the Bayesian approaches achieve higher coverage probabilities than existing methods. Finally, we use an engineering example to demonstrate all the proposed Bayesian credible estimations for the exponential distribution based on upper record value data.

Credibility Theory Under the Least Squared Relative Loss Function

ABSTRACTThe classical Bühlmann model employs a least squared loss criterion that penalizes pricing errors equally across all risk classes. In contrast, this paper develops a new credibility theory based on the least squared relative loss (LSRL) function to address scenarios where the classical approach may fall short. We derive explicit expressions of LSRL‐based credibility estimators, including non‐parametric versions and Bühlmann–Straub extensions. Through a comparative study, we illustrate the real‐world applicability of the LSRL estimator across different scenarios, highlighting its advantages and limitations in comparison to the classical model. Additionally, we explore different LSRL formulations to provide deeper insights into their practical viability.

Assurance Practices in Colombia’s Non-Financial Sectors: Enhancing Sustainability Report Reliability

This research enhances the reliability of sustainability reports in Colombia’s mining–hydrocarbons, construction, and manufacturing sectors. Amid growing demands for corporate transparency, this study evaluates assurance practices and proposes a model for measuring sustainability report reliability. Using bootstrapping regression, this study provides credible coefficient estimates without assuming a normal distribution. Key findings show that SDG application, assurance scope, and auditing firm consistency significantly influence report reliability, affirmed by 95% confidence intervals. This study’s pragmatic approach suggests best-case and worst-case scenarios for policymakers and companies to optimize report reliability. Furthermore, the proposed model paves the way for future research, with the International Standard on Sustainability Assurance (ISSA) 5000 by the International Auditing and Assurance Board (IAASB), potentially acting as a catalyst for mandatory sustainability reporting in Latin America. This proposed standard promises to enhance sustainability assurance practices. This research contributes to academic discourse on sustainability assurance and guides improvements in corporate reporting transparency and accountability. Future research should expand this model to other sectors and regions, validating its applicability and exploring broader temporal scopes to strengthen its empirical foundations.

Non-Linear Saturated Multi-Objective Pseudo-Screening Using Support Vector Machine Learning, Pareto Front, and Belief Functions: Improving Wastewater Recycling Quality

Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model them. Since wastewater treatment plants are large-scale operations, the limited opportunities for extensive experimentation may be offset by miniaturizing experimental schemes through the use of fractional factorial designs (FFDs). A recycling quality improvement study that relies on non-linear multi-objective multi-parameter FFD (NMMFFD) datasets was reanalyzed. A published NMMFFD ultrafiltration screening/optimization case study was re-examined regarding how four controlling factors affected three paper mill recycling characteristic responses using a combination of statistical and machine learning methods. Comparative machine learning screening predictions were provided by (1) quadratic support vector regression and (2) optimizable support vector regression, in contrast to quadratic linear regression. NMMFFD optimization was performed by employing Pareto fronts. Pseudo-screening was applied by decomposing the replicated NMMFFD dataset to single replicates and then testing their replicate repeatability by introducing belief functions that sought to maximize credibility and plausibility estimates. Various versions of belief functions were considered, since the novel role of the three process characteristics, as independent sources, created a high level of conflict during the information fusion phase, due to the inherent divergent belief structures. Correlations between two characteristics, but with opposite goals, may also have contributed to the source conflict. The active effects for the NMMFFD dataset were found to be the transmembrane pressure and the molecular weight cut-off. The modified adjustment was pinpointed to the molecular weight cut-off at 50 kDa, while the optimal transmembrane pressure setting persisted at 2.0 bar. This mixed-methods approach may provide additional confidence in determining improved recycling process adjustments. It would be interesting to implement this approach in polyfactorial wastewater screenings with a greater number of process characteristics.

The value of statistical life in air and road transport

PurposeAccidents occur in all transport modes and can cause significant material damage and loss of life. To efficiently allocate resources that enable the reduction of accidents, it is necessary to estimate the value of a statistical life (VSL). This study aims to show how the statistical value of human life in air and road traffic is determined.Design/methodology/approachThis study examines and applies recent empirical evidence aimed at clarifying the basic concept of the VSL and defining the reasons for its importance in evaluating public policies in aviation safety and road traffic safety. The research focuses on the principal equity indicators, both theoretical and empirical, that must be confronted to provide a credible estimate of the VSL.FindingsPrevious research shows that the VSL could have a considerable range of possible values. This study emphasizes that risk reduction funds need to be distributed in a consistent and equitable manner to achieve the best outcomes for society as a whole.Practical implicationsThis research will guide future research efforts towards understanding the impact of the estimated statistical life value on air and road safety implications.Social implicationsThis study strengthens awareness of the importance of the VSL in evaluating public policies in the field of aviation and road safety.Originality/valueTo the best of the authors’ knowledge, this study is one of the first to compare air and road traffic from the perspective of the VSL evaluation.

Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion

Abstract Credible regional carbon budget estimates from atmospheric CO2 measurements rely on the accuracy of atmospheric transport models (ATMs). However, the atmospheric transport in ATMs is usually simplified and spatiotemporally averaged, leading to systematic biases in simulating the atmospheric CO2 and estimating surface CO2 fluxes. We show that forward simulations of global CO2 using an ATM, GEOS-Chem, at a native resolution of 0.5°×0.625° and a coarse resolution of 4°×5° differ significantly near the surface in the Northern Hemisphere and the polar vortex, mainly because of advection in GEOS-Chem. Comparing observing system simulation experiments that assimilate synthetic observations sampled from the forward simulations, we separate the impact of coarse-resolution GEOS-Chem on regional flux estimates. The results suggest that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and northern and southern extratropics, respectively. In addition, these errors lead to an underestimated seasonal amplitude in the northern extratropical land and a reversed sign of the seasonal cycle in the northern extratropical ocean. The reversed sign of the seasonal cycle has also been observed in a real data assimilation experiment and several state-of-the-art inversions, suggesting that reasonable ocean flux estimates depend strongly on the accuracy of the ATM.

Estimation and Bayesian Prediction of the Generalized Pareto Distribution in the Context of a Progressive Type-II Censoring Scheme

The generalized Pareto distribution plays a significant role in reliability research. This study concentrates on the statistical inference of the generalized Pareto distribution utilizing progressively Type-II censored data. Estimations are performed using maximum likelihood estimation through the expectation–maximization approach. Confidence intervals are derived using the asymptotic confidence intervals. Bayesian estimations are conducted using the Tierney and Kadane method alongside the Metropolis–Hastings algorithm, and the highest posterior density credible interval estimation is accomplished. Furthermore, Bayesian predictive intervals and future sample estimations are explored. To illustrate these inference techniques, a simulation and practical example are presented for analysis.

Child Health and Mortality Prevention Surveillance (CHAMPS): Manhiça site description, Mozambique

The Manhiça Health Research Centre (Manhiça HDSS) was established in 1996 in Manhiça, a rural district at Maputo Province in the southern part of Mozambique with approximately 49,000 inhabited households, a total population of 209.000 individuals, and an annual estimated birth cohort of about 5000 babies. Since 2016, Manhiça HDSS is implementing the Child Health and Mortality Prevention Surveillance (CHAMPS) program aiming to investigate causes of death (CoD) in stillbirths and children under the age of 5 years using an innovative post-mortem technique known as Minimally Invasive Tissue sampling (MITS), comprehensive pathogen screening using molecular methods, clinical record abstraction and verbal autopsy. Both in-hospital and community pediatric deaths are investigated using MITS. For this, community-wide socio-demographic approaches (notification of community deaths by key informants, formative research involving several segments of the community, availability of free phone lines for notification of medical emergencies and deaths, etc.) are conducted alongside to foster community awareness, involvement and adherence as well as to compute mortality estimates and collect relevant information of health and mortality determinants. The main objective of this paper is to describe the Manhiça Health and Demographic Surveillance System (HDSS) site and the CHAMPS research environment in place including the local capacities among its reference hospital, laboratories, data center and other relevant areas involved in this ambitious surveillance and research project, whose ultimate aim is to improve child survival through public health actions derived from credible estimates and understanding of the major causes of childhood mortality in Mozambique.

Quantile balanced credibility model

This paper introduces a model into the methodology of greatest accuracy approach in credibility theory, we extend Pitselis (2013) quantile credibility model by including a different type of losses which is called the weighted balanced loss function (WBLF henceforth) in order to establish novel credibility premiums under the balanced squared error loss function. More specifically, we further explain how quantiles under the WBLF can be incorporated into the classical Bühlmann’s (1967) credibility model. A new model of quantile credibility under the WBLF is presented, where the premiums are estimated using the non-parametric approach where premiums must be estimated based on data only, without postulating any type of distribution. Furthermore, we establish the credibility estimators and provide an application to real data of the results using R software to evaluate the effectiveness of our proposed model, and conclude with a comparison with the Pitselis (2013) model and the Bühlmann (1967) model to show the effectiveness of our premium.

Assessing the Impact of a Designated Pharmacist Intervention on Drug Treatment Costs and Technical Efficiency in the Hemato-Oncology Outpatient Clinic

ObjectivesThis study aimed to investigate the impact of a designated pharmacist (DPha) intervention in a hemato-oncology unit, focusing on reducing drug treatment costs and improving technical efficiency (TE). MethodsData from an 8-month intervention in the Israeli Clalit Health Services hemato-oncology outpatient unit were analyzed. During the study, the DPha reviewed the drug therapies being administered. After the review, a recommendation letter was sent, if relevant, to the treating physician. Data on drug treatment costs and interventions were meticulously collected and analyzed from the perspective of the insurer. A simple design was used to assess the DPha intervention’s contribution to TE and cost reduction, which was used to generate credible and transparent estimates. Sensitivity analyses were conducted to assess the robustness of 2 major variables: drug prices and pharmacist salaries. ResultsOver 8 months, DPha interventions led to a $279 191 cost reduction for 91 patients, resulting in net savings of $269 420 ($2960 per patient). Noteworthy is the $411 savings for each hour worked by the pharmacist, with a major impact on medications not insurer approved for the patient’s condition ($101 151) and discontinuing inappropriate medications ($52 681). Biological drug optimization accounted for 81% of total savings. Sensitivity analyses demonstrated significant cost savings across various drug prices and pharmacist salary scenarios. ConclusionsThe study proposes a practical framework for optimizing pharmacist services and reducing the inappropriate use of costly oncology medications. Incorporating a DPha enhances TE and yields significant cost reductions, offering valuable insights for insurers, policy makers, and healthcare professionals.

Robust r-process Nucleosynthesis beyond Lanthanides in the Common Envelop Jet Supernovae

The common envelop jet supernovae (CEJSNe) r-process scenario has been proposed as an r-process nucleosynthesis site in the past decade. Jets launched by a neutron star that spirals in inside the core of a red supergiant star in a common envelope evolution supply the proper conditions for forming elements heavier than iron through the rapid neutron-capture process. The present work initially unveils the r-process abundance patterns that result from the density profile in the relatively long-lived jets. The results indicate that the expansion profile unique to the CEJSN scenario can produce the largest ratio of the third r-process peak elements to lanthanides among current r-process scenarios and, in addition, can form quite an amount of lanthanides, about 1% of matters above the first peak, in a single event. The comparison of the ratio of the third-peak elements to the lanthanides with several observed r-enhanced metal-poor stars and with other r-process scenarios suggests that a high mass of third-peak elements is anticorrelated with a high fraction of lanthanides, both in observations and theory. The CEJSN-like scenario plays a significant role in this conclusion since it reproduces the observational features of some particular r-enhanced metal-poor stars where other r-process scenarios encounter problems. Due to the formation of extremely heavy elements, the CEJSN also offers a credible estimation of the age of the most actinide-boosted star by cosmochronometry.

Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters

Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna over a wide range of spatial and temporal scales. Spatial predictions of abundance for 67 taxa were developed, using an extensive dataset of faunal abundances collected using a towed camera system and spatially explicit predictor variables including bottom-trawl fishing effort, using a Joint Species Distribution Model (JSDM). The model fit metrics varied by taxon: the mean tenfold cross-validated AUC score was 0.70 ± 0.1 (standard deviation) for presence–absence and an R2 of 0.11 ± 0.1 (standard deviation) for abundance models. Spatial predictions of probability of occurrence and abundance (individuals per km2) varied by taxon, but there were key areas of overlap, with highest predicted taxon richness in areas of the continental shelf break and slope. The resulting joint predictions represent significant advances on previous predictions because they are of abundance, allow the exploration of co-occurrence patterns and provide credible estimates of taxon richness (including for rare species that are often not included in more commonly used single-species distribution modelling). Habitat-forming taxa considered to be Vulnerable Marine Ecosystem (VME) indicators (those taxa that are physically or functionally fragile to anthropogenic impacts) were identified in the dataset. Spatial estimates of likely VME distribution (as well as associated estimates of uncertainty) were predicted for the study area. Identifying areas most likely to represent a VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of vulnerable habitats, and facilitates an evidence-based approach to managing potential impacts of bottom-trawling.

Mars Ascent Propellants and Life Support Resources - Take it or Make it?

Studies of Mars missions over the past thirty years lacked credible cost estimates, so the total mass of materiel delivered to Low-Earth Orbit (LEO) was typically used as a rough measure of relative mission cost because the complexity of the mission was thought to be roughly proportional to the initial mass in LEO (IMLEO). Historically, high launch costs led to large investments in space hardware development which led to high space mission costs. Reducing mass became the central theme of space mission engineering. We are now entering a new era where launch costs no longer have the impact that they would have two decades ago. Launch costs are coming down to the point where we must ask ourselves whether it now makes sense to bring ascent propellants and life support resources from Earth (with higher reliability as a bonus), as opposed to using in situ propellant production and cycling of life support resources. This paper compares various options for bringing ascent propellants and life support resources from Earth vs. developing in situ. In short, it examines the “take it or make it” options for both technologies. For ISPP, the answer is clear: Mars ISPP is not worth the investment when launch costs are low. For life support, the most robust option is to bring life survival resources from Earth, and only use cycling to upgrade the quality of life for the crew.

Does a palliative medicine service reduce hospital length of stay and costs in adults with a life-limiting illness?-a difference-in-differences evaluation of service expansion in Ireland.

People approaching end of life account disproportionately for health care costs, and the majority of these costs accrue in hospitals. The economic evidence base to improve value of care to this population is thin. Natural experiment methods may be helpful in bridging evidence gaps with credible causal estimates from routine data, but these methods have seldom been applied in this field. This study aimed to evaluate the association between timely palliative care consultation and length of stay for adults with serious illness admitted to acute hospital in Ireland. In primary analysis we evaluated if timely palliative care receipt following emergency hospital inpatient admission impacted length of stay (LOS); in secondary analysis we verified if palliative medicine service (PMS) implementation co-occurred with any changes in in-hospital mortality, and we estimated cost differences associated with any change in LOS. This was a secondary analysis on routinely collected data for acute admissions to public hospitals in Ireland. We used difference-in-differences analysis to exploit the staggered implementation of PMS teams at acute public hospitals in Ireland between 2010 and 2015. We identified palliative care receipt following PMS implementation using ICD-10 codes, and we matched admissions involving a palliative care interaction to admissions in years prior to PMS implementation using propensity score weights. Our primary analytic sample included 4,314 observations, of whom 608 (14%) received timely palliative care. We estimated that the intervention reduced LOS by nearly two days, with an estimated associated saving per admission of €1,820. These analyses were robust to multiple sensitivity analyses on regression specification, weighting strategy and site selection. Proportion of admissions ending in death did not change following PMS implementation. Prompt interaction between suitable patients and palliative care can improve the quality and efficiency of care to this population. Many patients receive palliative care later in the hospital stay, which does not yield cost-savings. Future studies can extend and strengthen our approach with better data, as well as using different methods to understand how to trigger palliative care early in a hospital admission and realise available gains.

Psychometric Properties of the Sexual Services Acts Materials for Pay (SSAMP) Index: Identifying the Virtual, In-Person, and Material Sex Trades for Financial Compensation

ABSTRACT Exchanging sex for financial compensation is thought to be underreported primarily because extant studies (1) use a single item to capture a complex, stigmatized phenomenon, and (2) do not capture the complex types or reasons why people engage in the sex trades. Few survey studies have explored the sex trades among university students. This study evaluated the psychometric properties of a newly developed measure, the Sexual Services Acts and Materials for Pay (SSAMP), among university students. Surveys were administered to undergraduate (N = 544, time 1; N = 362, time 2) and graduate (N = 331, time 1; N = 187, time 2) students two weeks apart at a predominantly White, public, Midwestern university. Findings suggested that our index had (1) strong convergent validity with the single item used in prior studies, (2) identified more cases of sex trading than the single item, (3) good internal consistency reliability (4) moderate to strong test-retest reliability, and (5) strong discriminant validity. Exploratory factor analysis revealed that items loaded above .59 on a single factor. To our knowledge, this study is the first to evaluate a multi-item sex trades measure in the U.S. Future research should continue to advance the SSAMP and adapt this index to provide credible estimates and nuanced understanding of the sex trades across contexts.

Discussion on “Sample Size Determination for Credibility Estimation,” by Liang Hong, Volume 26(4)

Discussion on “Sample Size Determination for Credibility Estimation,” by Liang Hong, Volume 26(4)

Fuzzy Multi-Item Newsvendor Problem: An Application to Inventory Management

This paper proposes a novel approach to the fuzzy newsvendor problem for inventory management applications. The main contributions of the paper are the following: a new credibility estimation is proposed, to explore the neighborhood around the most impactful demand scenarios; a simulation procedure was designed for the different demand scenarios, which allows comparison of the proposed approach with classical and fuzzy multi-item newsvendor problems; a modified genetic algorithm (GA) is introduced to ameliorate previous genetic algorithms in both the generation and evaluation of solutions. The new formulation of the fuzzy newsvendor problem, together with the modified GA, were shown to improve the average profit by up to 55% in problems with low-budget scenarios.

Valuing the Disamenity of Open Dumpsites: A Sri Lankan Case Study

Disposal of municipal solid waste in open dumpsites remains widespread throughout the developing world. While harm to local environments and public health may seem apparent, the lack of credible estimates of damages from open dumpsites is a major reason for their continued use. This paper estimates the disamenity damages imposed by an open dumpsite in Colombo, Sri Lanka, on nearby property owners using hedonic regression methods and a novel data set collected for the purpose. We find that property values increase by 10% for every additional 100 m of distance from the edge of the dumpsite and thus provide the first credible estimate of costs imposed by open dumping in a developing country. As open dumpsites are often sited in less affluent areas, our results suggest that such sites are likely exacerbating urban inequities in urban agglomerations across the developing world.