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Orthogonal‐array composite designs (OACDs) and orthogonal‐uniform composite designs (OUCDs) are orthogonal composite designs that combine two‐level full or fractional factorial and three‐level orthogonal‐array/uniform designs for estimation of the linear, bilinear, and quadratic effects in a second‐order response surface model. In this study, the effects of missing one observation in the various design portions (factorial (f) axial (a) and center (c)), on the precision of parameter estimates, prediction variance and design efficiency of OACDs and OUCDs for 5 ≤ k ≤ 9 factors at different values of α (the distance of a non‐zero co‐ordinate in an additional design point from the center) are evaluated. The results showed that missing a factorial and an axial point have adverse effect on the precision of parameter estimates of OACDs and OUCDs, while missing a center point has little effect. Missing an axial point caused the highest effect on the prediction variance and design efficiencies. The FDS plots showed OACDs to be better designs for k ≤ 7 and OUCDs for k = 8 and 9 factors. [ABSTRACT FROM AUTHOR]

The problem of local item dependencies (LIDs) is very common in personality and attitude measures, particularly in those that measure narrow-bandwidth dimensions. At the structural level, these dependencies can be modeled by using extended factor analytic (FA) solutions that include correlated residuals. However, the effects that LIDs have on the scores based on these extended solutions have received little attention so far. Here, we propose an approach to simple sum scores, designed to assess the impact of LIDs on the accuracy and effectiveness of the scores derived from extended FA solutions with correlated residuals. The proposal is structured at three levels—(a) total score, (b) bivariate-doublet, and (c) item-by-item deletion—and considers two types of FA models: the standard linear model and the nonlinear model for ordered-categorical item responses. The current proposal is implemented in SINRELEF.LD, an R package available through CRAN. The usefulness of the proposal for item analysis is illustrated with the data of 928 participants who completed the Family Involvement Questionnaire-High School Version (FIQ-HS). The results show not only the distortion that the doublets cause in the omega reliability estimate when local independency is assumed but also the loss of information/efficiency due to the local dependencies. [ABSTRACT FROM AUTHOR]

In today's data‐driven decision‐making era, acquiring accurate information is vital. However, survey research faces challenges with sensitive issues. To address this, the Composite Randomized Response Technique (CRRT) was introduced in estimating the proportion of respondents possessing sensitive attributes. This study revealed that as the model captures more and more people involved in the sensitive attributes (πs) from 0.1 to 0.4, the relative efficiency of CRRT increases from 2.2217 to 678.7843. Hence, CRRT was found to be more efficient than the conventional model, making it a robust approach for surveys targeting sensitive attributes, enhancing data accuracy, and supporting effective policy evaluation and resource allocation. [ABSTRACT FROM AUTHOR]

In the cluster randomized crossover trial, a sequence of treatment conditions, rather than just one treatment condition, is assigned to each cluster. This contribution studies the optimal number of time periods in studies with a treatment switch at the end of each time period, and the optimal number of treatment switches in a trial with a fixed number of time periods. This is done for trials with a fixed number of clusters, and for trials in which the costs per cluster, subject, and treatment switch are taken into account using a budgetary constraint. The focus is on trials with a cross-sectional design where a continuous outcome variable is measured at the end of each time period. An exponential decay correlation structure is used to model dependencies among subjects within the same cluster. A linear multilevel mixed model is used to estimate the treatment effect and its associated variance. The optimal design minimizes this variance. Matrix algebra is used to identify the optimal design and other highly efficient designs. For a fixed number of clusters, a design with the maximum number of time periods is optimal and treatment switches should occur at each time period. However, when a budgetary constraint is taken into account, the optimal design may have fewer time periods and fewer treatment switches. The Shiny app was developed to facilitate the use of the methodology in this contribution. [ABSTRACT FROM AUTHOR]

In this paper, we aim to modify multivariate copula-based conditional quantiles for a targeted random variable, given multiple random variables attaining their respective quantiles. Specifically, we propose two modifications through the Cornish–Fisher expansion, one of which involves its analytic higher-order unconditional moments. The second one accounts for its higher-order conditional moments estimated using a ratio estimation method. By considering multivariate elliptical and Archimedean copulas with Johnson's SU margins, our simulation study shows that this method provides more accurate conditional moment estimators compared to the naive method. They result in expanded conditional quantile estimators, whose efficiency is relatively better than their unexpanded versions, particularly at lower and higher quantile levels under a stronger (tail) dependence assumption. A much higher efficiency is gained when the first modification is performed. These findings are validated using an empirical study based on cryptocurrency return data and a comparative analysis against the existing estimation approaches. [ABSTRACT FROM AUTHOR]

The volume under the receiver operating characteristic (ROC) surface (VUS) is a natural generalization of a classical tool, the area under the ROC curve from a disease with two statuses (e.g., healthy and diseased) to a disease with a three-class status (e.g., healthy, intermediate, and diseased) for evaluating the effectiveness of a continuous biomarker in discriminating the disease status. In this work, we discuss the problem of estimating VUS using ranked set sampling (RSS), a cost-efficient alternative to simple random sampling (SRS), which is applicable in situations in which the actual quantification of the biomarker is hard, time-consuming, costly or tedious but a small number of sample units can still be ordered without referring to their precise values. We develop several nonparametric estimators when SRS or RSS design is applied to each of the healthy, intermediate and diseased subpopulations. We study the properties of the proposed estimators, including unbiasedness, variance expression, asymptotic normality, and efficiency. Specifically, we show that the introduced estimators are at least as efficient as their SRS counterparts and often far more efficient under a large class of imperfect ranking models. Lastly, to demonstrate the applicability and efficiency of the introduced procedures in an environmental context, we apply them to a real environmental dataset, utilizing three of its five classes. [ABSTRACT FROM AUTHOR]

Transformations in agriculture not only change the ways in which natural resources and social capital are used but are also a prerequisite for different opportunities to create added value and improve the viability and sustainability of rural areas. The purpose of this article is to assess the differences and effects of transformations in agriculture in the Bulgarian regions. Based on statistical data from the Censuses of agricultural holdings in 2010 and 2020, the DEA method was applied. Six models (three input-oriented and three output-oriented) were constructed and tested, and the efficiency coefficients were assessed on a regional level. The degree of structural changes and the efficiency of the used production and other resources were evaluated, and conclusions were drawn. In the majority of the models, differences were observed between the Northern regions of the country and the South Western and South Central regions. The most significant are the efficiency coefficient of the labor force used, and the output produced, the gross value added, and the net mixed income. In the other models, multidirectional changes were observed. The North Western is the region in which all calculated efficiency coefficients increased, and in the North Eastern and South Eastern regions, the most calculated coefficients decreased. Based on the analysis, recommendations related to region-specific agricultural policies for better resource allocation and sustainable development are proposed. [ABSTRACT FROM AUTHOR]

Abstract Time-based surveys often experience missing data due to several reasons, like non-response or data collection limitations. Imputation methods play an essential role in incorporating these missing values to secure the accuracy and reliability of the survey outcomes. This manuscript proposes some optimal class of memory type imputation methods for imputing missing data in time-based surveys by utilizing exponentially weighted moving average (EWMA) statistics. The insights into the optimal conditions for incorporating our proposed methods are provided. A comprehensive examination of the proposed method utilizing simulated and real-life datasets is conducted. Comparative analyses against the existing imputation methods exhibit the superior performance of our methods, particularly in the scenarios characterized by developing trends and dynamic response patterns. The outcomes highlight the effectiveness of utilizing EWMA statistics into memory type imputation methods, displaying their flexibility to changing survey dynamics.

The mean residual lifetime (MRL) of a unit is its expected additional lifetime provided that it has survived until time t. The MRL estimation problem has been frequently addressed in the literature since it has wide applications in statistics, reliability and survival analysis. In this paper, we consider the problem of estimating the MRL in ranked set sampling when actual quantifications of a concomitant variable are available. To exploit the additional information of the concomitant variable, we introduce several MRL estimators based on some regression techniques. We then compare them with the standard MRL estimator in simple random sampling using Monte Carlo simulation and a real dataset from the Surveillance, Epidemiology, and End Results Program. Our results indicate the superiority of the procedures that we have developed when the quality of ranking is fairly good. [ABSTRACT FROM AUTHOR]

Copyright of Dirasat: Educational Sciences is the property of University of Jordan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We develop a collection of unbiased estimators for the proportion of a population bearing a sensitive characteristic using a randomized response technique with two decks of cards for any choice of weights. The efficiency of the estimator depends on the weights, and we demonstrate how to find an optimal choice. The coefficients of skewness and kurtosis are introduced. We support our findings with a simulation study that models a real survey dataset. We suggest that a careful choice of such weights can also lead to all estimates of proportion lying between [0, 1]. In addition, we illustrate the use of the estimators in a recent study that estimates the proportion of students, 18 years and over, who had returned to the campus and tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

Abstract–In the regression analysis, ordinary least square techniques is commonly used. However, the data's outcomes may be untrustworthy if there is an outliers in it. In order to deal with the outliers problem, robust quantile regression methods have been frequently presented as alternatives to OLS for a long time. In this article, primarily a exponential ratio-type estimators is suggested. After that, robust quantile regression estimators are proposed, that is a useful strategy. The application of robust quantile regression empowered the efficiency of the estimators especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulations studies are performed to support our theoretical findings. [ABSTRACT FROM AUTHOR]

The ranked set sampling (RSS) method is an efficient sampling method that is used when the judgment ranking is easy while measuring the sampling units is expensive. This method has a higher efficiency than the commonly used simple random sampling (SRS) method. If this method is implemented in two stages, it will be called double-ranked set sampling (DRSS), which has a higher efficiency than RSS. Recently, the except extreme ranked set sampling (EERSS) method is proposed as a modification of the RSS. In the current study, we modified the EERSS and suggested the double-stage EERSS (DEERSS) method for estimating the population mean. The DEERSS estimator is compared with each DRSS, EERSS, RSS, and SRS counterparts. It has been shown that the DEERSS estimator is more efficient than all the competitor estimators considered in this study. Also, it is shown that this estimator is unbiased in the case of symmetrical distributions. Three data sets are employed to test the applicability and efficiency of the DEERSS method. [ABSTRACT FROM AUTHOR]

This article aims to examine a novel combined power and refrigeration system, using renewable and waste heat sources suitable for low-temperature applications. The present system is an integrated Kalina cycle and ejector refrigeration system to generate power and refrigeration simultaneously. To improve the vapour generation, the separator vapour fraction is used as a decision variable. Relative irreversibility and efficiency defect as two important parameters considered in this system for an investigation to identify the weaker components. The combined system generates power and refrigeration with two different mediums by the incorporation of the heat exchanger at the turbine exhaust. The novel system's energy and conventional exergy evaluation are carried out through Python Software. The optimum values of decision variables: turbine concentration, separator vapour fraction, entrainment ratio, expander ratio, split ratio and turbine concentration are identified using Python software from an opted range of variables. The maximum value of net power output, first law efficiency for power generation system, combined system, second law efficiency for power generation system, combined system, refrigeration effect and coefficient of performance are obtained as 113 kW, 8.85%, 11.83%, 93.44%, 81.29%, 38.07% and 0.118, respectively, at higher separator vapour fraction. Among the components considered in the combined power generation system, the condenser and LTRGN account for the higher exergy destruction rate of 30.41% and 25.53%. The coefficient of performance is maximized at a higher value of the refrigeration effect. The turbine pressure at the inlet is increased with increments in turbine work on choosing the higher value of the expander ratio. The higher exergetic value components are not emphasized to focus on improvement. [ABSTRACT FROM AUTHOR]

AbstractOutliers are outrageous values that occur in an experiment and can almost be inevitable. The presence of outliers in an experiment can affect the results of a response surface design due to disruption in the design’s structure. In this paper, a new class of orthogonal uniform composite designs (OUCD4) for fitting third-order response surface model are constructed using the minimax outlying effect criterion that minimizes the maximum outlying effect. These constructed designs referred to as orthogonal uniform composite minimax outlying (OUCO4) designs are robust to a single outlier. The OUCO4 designs are shown to be more efficient in terms of relative efficiency compared to the OUCD4. [ABSTRACT FROM AUTHOR]

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators. [ABSTRACT FROM AUTHOR]

Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs. [ABSTRACT FROM AUTHOR]

In this article, two new estimators of population proportion of a sensitive characteristic are introduced by using a method analogous to Analysis of Variance (ANOVA). Then, a new unbiased regression type estimator is developed by utilizing these two estimators. The proposed estimator is, then, compared with its competitor at the same level of protection of the respondents. Also included is a study, based on data collected during summer 2021, of the currently hot topic of estimating the proportion of students, 18 years and older, returning to schools in fall 2021, who tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined and separate estimators are also developed for estimating the finite population variance using supplementary information in terms of one or two auxiliary variables. An empirical study is carried out to study the effect of measurement error on the relative efficiencies of the proposed estimators. [ABSTRACT FROM AUTHOR]

The area under a receiver operating characteristic (ROC) curve is frequently used in medical studies to evaluate the effectiveness of a continuous diagnostic biomarker, with values closer to one indicating better classification. Unfortunately, the standard statistical procedures based on simple random sampling (SRS) and ranked set sampling (RSS) techniques tend to be less efficient when the values of the area under a ROC curve (AUC) get closer to one. Thus, developing some statistical procedures for efficiently estimating the AUC when it is close to one is very important. In this paper, some estimators are developed using nomination sampling to assess AUC. The proposed AUC estimators are compared with their counterparts in SRS and RSS using Monte Carlo simulation. The results show that some of the estimators developed in this study considerably improve the efficiency of the AUC estimation when it is close to one. This substantially reduces the cost and time for the sample size needed to obtain the desired precision. [ABSTRACT FROM AUTHOR]

The RSS approach is a useful method of sampling that reduces the cost and improves the representativeness of the population. It provides more efficient estimators than the competitors based on SRS. However, using RSS could be a difficult task to observe all the ranks. Thus, using only the extreme ranks eases the task and reduces the error in ranking. Samawi et al. (1996) proposed the method of Extreme Ranked Set Sampling (ERSS) to reduce errors in ranking and showed that the method gives an unbiased estimate of the population mean in the case of symmetric populations and it provides a more efficient estimator than SRS. However, the estimator of this method is biased when the distribution is skewed. Many researchers have considered ERSS, investigated several estimators, and studied their properties. In this paper, we adopt the ERSS technique when the samples are drawn from the Rician distribution. Several estimators have been studied, including arithmetic mean, geometric mean, harmonic mean, quadratic mean, median, variance, mean deviation, skewness, and kurtosis. Computer simulations were used to check the properties of these estimators and compared with the corresponding estimators using SRS. Some estimators based on ERSS are more efficient than the corresponding estimators from SRS, but some others are not. [ABSTRACT FROM AUTHOR]

The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population. [ABSTRACT FROM AUTHOR]

In this paper, a new improved calibration estimator for estimating the population mean in the stratified random sampling using standard deviation of the auxiliary variable is proposed. A simulation study was conducted to assess the performance of the estimators in symmetric and skewed populations using absolute relative bias, mean square error and percentage relative efficiency. The results showed that the proposed estimator is more efficient compared to the existing estimators considered in this work. [ABSTRACT FROM AUTHOR]

The present article deals with the application of the Data Envelopment Analysis (DEA) methodology to identify the most weighty factors that are associated with student performance on large-scale assessments, amongst them, the permanent assessment system for basic education "SPAECE" test. The DEA Slacks-Based-Measure (SBM) model was used to estimate the relative efficiency of school units in the city of Sobral (CE), one of the most prominent Brazilian counties in the educational scenario. It was evident that the presence of libraries, computer labs, sports courts and rooms for special care in school units constitutes a significant factor associated with the high performance of students, impacting, therefore, school efficiency. [ABSTRACT FROM AUTHOR]

Missing observations are practical problems that occur frequently even in a well-planned experiment and can significantly impact the statistical accuracy of the experiment. This work introduces a new class of third-order designs called augmented orthogonal uniform composite minimax loss (AOUCM) designs, which are more robust to a single missing design point as a variation of the existing third-order augmented orthogonal uniform composite designs (AOUCDs). The AOUCM designs are constructed using the minimax loss criterion. The constructed AOUCM designs are evaluated and compared with AOUCDs based on the relative D- and G-efficiency criteria, generalized scaled deviation, and the fraction of design space plot. The AOUCM designs are shown to be robust and more efficient in estimating the parameters of the third-order model. Moreover, although the AOUCDs and AOUCM designs are stable and uniformly distributed throughout the design space, the AOUCM designs have the least scaled prediction variance. [ABSTRACT FROM AUTHOR]

Insurance companies play a pivotal role in the financial systems of developing countries, wielding substantial influence on systemic financial stability. Thus, understanding their efficiency, performance, and sustainability is paramount for policymakers and stakeholders alike. The aim of this paper is to evaluate the relative efficiency of insurance companies within the North Macedonian market spanning the years 2018 to 2022. Employing the input-oriented BCC DEA model, the study integrates capital and labour as inputs, while assessing risk-pooling/bearing services and intermediate function as outputs. Our findings underscore the fluctuating efficiency levels within North Macedonia's insurance sector. Notably, the sector exhibited its peak efficiency in 2018 at 83.62%, dipping to its lowest point of 73.81% in 2020. Moreover, discerning between life and non-life insurers, we observe an average relative efficiency of 0.8067 for non-life insurers, contrasted with a higher average efficiency score of 0.9011 for life insurance companies over the examined period. This study contributes significantly on multiple fronts. Firstly, it pioneers empirical investigation of the efficiency on the North Macedonian insurance market, encompassing pre- and post-COVID efficiency metrics. This fills a notable gap in the literature, particularly within the context of emerging European markets. Secondly, our comprehensive approach facilitates a holistic evaluation of the insurance sector's performance across a five-year span, offering insights into its overarching dynamics and efficacy. Thirdly, the implications of our findings extend to policymakers, regulators, and insurance company management, aiding in informed decision-making and strategic planning. [ABSTRACT FROM AUTHOR]

Summary In this paper, we begin by reviewing the optional randomised response technique estimator (ORRTE) developed by Chaudhuri and Mukerjee for estimating the proportion of a sensitive characteristic in a population. We show that their estimator is unbiased and has smaller variance than the Warner's estimator. Then we make an attempt at developing an optimised optional randomised response technique estimator (OORRTE). The proposed OORRTE is shown to be more efficient than the ORRTE. Findings from simulation studies are discussed and interpreted for various situations. Sample sizes for the Warner's estimator, the ORRTE and the OORRTE are computed based on power analysis introduced by Ulrich, Schroter, Striegel and Simon. Finally, we include an application to real data on COVID‐19 by considering it to be partially sensitive variable; that is, sensitive to some but not to others. The data used are included in the paper and the R‐codes used in the simulation study are documented in online material. [ABSTRACT FROM AUTHOR]

This article addresses the problem of estimating the population distribution function for stratified random sampling in the presence of non-response. We suggest a generalized class of estimators for estimating the finite cumulative distribution function using the auxiliary information. Expressions for bias and mean squared error are derived up to the first order of approximation. The performance of the estimators are compared both theoretically and numerically. A real data set is used to support the theoretical findings. [ABSTRACT FROM AUTHOR]

We propose two types of estimators that are analogous to Franklin's model. One estimator is derived by concentrating on the row averages of the responses, and another is obtained by concentrating on the column averages of the observed responses. In the latter case we have two responses per respondent from a bi‐variate normal distribution. The proposed estimator based on row averages, by making use of negatively correlated random numbers from a multivariate density, is always more efficient than the corresponding Franklin's estimator. In the case of the proposed estimator based on column averages, we found that the use of positively correlated random numbers from a bivariate density can lead to the most efficient estimator. We also discuss results which are observed by making use of three responses per respondent. When the three responses are recorded, three independent normal densities are derived from three correlated variables. The findings are supported based on analytical, numerical, and simulation studies. A simulation study was done to determine the minimum sample size required to produce nonnegative estimates of the population proportion of a sensitive characteristic, and to investigate the 95% nominal coverage by the interval estimates. Ultimately at the end, one best estimator is suggested. A very neat and clean derivations of theoretical results and discussion of numerical and simulation studies are documented in Data S1. [ABSTRACT FROM AUTHOR]

In this paper, we consider an inference problem in generalized exponential Ornstein–Uhlenbeck processes with change-point in the context where the dimensions of the drift parameter are unknown. The proposed method generalizes the work in recent literature for which the change-point has never been considered. Thus, in addition to taking care of possible chock, we study the asymptotic properties of the unrestricted estimator, the restricted estimator, and shrinkage estimators for the drift parameters. We also derive an asymptotic test for change-point detection and we establish the asymptotic distributional risk of the proposed estimators as well as their relative efficiency. Further, we prove that the proposed methods improve the goodness-of-fit. Finally, we present the simulation results which corroborate the theoretical findings and we analyze a financial market data set. [ABSTRACT FROM AUTHOR]

In regression analysis, the ordinary least squares (OLS) technique is a fundamental tool that is frequently used to model relationships between variables. If the data set contains an outlier, this technique could produce inaccurate outcomes. Outliers, or data points that are significantly different from the rest of the data, can lead to incorrect statistical interpretation. Systematically addressing outliers improves data precision and efficiency prior to drawing conclusions. Outliers may be misleading and cause statistical findings to be interpreted incorrectly. Prior to drawing conclusions from the data, it can be improved to acquirerelevant information by dealing with outliers effectively. The normality assumption is an essential statistical presumption that issusceptible to the effect of outliers in the context of traditional least squares regression. On the other hand, robust regression is a method used to estimate the regression coefficients in a linear regression model wherein the data contains outliers in an effort to improve the accuracy of the estimations. In the context of sample surveys, robust regression methods have been commonly suggested as useful alternatives to ordinary least squares for dealing with outliers. A novel exponential estimator for estimating the finite population mean is presented in this paper, which uses robust regression techniques while taking advantage of the known parameters of an auxiliary variable associated with the study variable. Assuming that the data contains outliers and that outliers have no effect on these estimators, as we are using the robust regression method, which is obtained by Huber M-estimation, our suggested estimators' mean squared errors are determined theoretically and compared to the ordinary least squares techniques. The numerical illustration and simulation results demonstrate that, when using the robust regression method, the suggested estimators perform well in the presence of outliers. [ABSTRACT FROM AUTHOR]

In this paper, we extend the optional randomized response technique (ORRT) developed by Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988] to the situation of estimating the proportion of two sensitive characteristics and their overlap. Lee, Sedory and Singh [Estimating at least seven measures of qualitative variables from a single sample using randomized response technique. Stat Prob Lett. 2013;83(1):399–409; Estimation of odds ratio, attributable risk, relative risk, correlation coefficient and other parameters using randomized response techniques. Behaviormetrika. 2021;48:371–392.] have shown that their crossed model performs better than their simple model from an efficiency point of views. Here we investigated a further improvement in the crossed model along the lines of Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988]. New unbiased estimators are proposed, their variance expressions are derived and estimators of variances are suggested. Lastly, we carry out a simulation study to investigate the behaviour of the proposed estimators with respect to their competitors. [ABSTRACT FROM AUTHOR]

This study explores the efficiency of intellectual capital (ICE) and working capital management (WCME) in the GCC industrial sector and its potential impact on firm performance. The data were gathered from Standard & Poor's database from 2015 to 2019. This study uses data envelopment analysis (DEA), regression analysis, and robustness tests to accomplish its aims. The results indicate that most firms do not employ their intellectual and working capital investments well and need improvement actions to achieve the best practices. The regression model results reveal that ICE and WCME significantly and positively influence firms' performance. The results of this study support the resource-based, trade-off, and pecking order theories. The study findings have important implications for many stakeholders; for example, they would be helpful for firm decision-makers in managing their investments in intellectual and working capital to achieve the best practices and improve a firm's performance. In addition, the findings would be helpful for financiers, because high-performance firms are likely to have more reasonable valuations that facilitate debt financing. Moreover, the findings have noteworthy implications for trading procedures as investors aspire to attractive economic returns for their investments in corporations that pasture ICE and WCME issues. Additionally, these findings have important implications for employee job satisfaction and retention by improving IC management. [ABSTRACT FROM AUTHOR]

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to a MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods, like least squares, minimum Hellinger distance, and optimal bounded influence function, available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), pecifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of the MTuM is established by employing the central limit theorem and validating it through a comprehensive simulation study. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to present a model for the efficiency assessment of smart cities based on 38 indicators (ISO standard 37120, ISO standard 37122 and additional indicators) in six dimensions of a smart city in order to produce a ranking of 127 cities in Croatia. Methodology: In this study, the Data Envelopment Analysis (DEA) method was used, which was preceded by the translator invariance method due to the standardization of 38 absolute values. The analysis was performed using the input-oriented BCC model. The input values are previously formed indices for six dimensions of smart cities; the index of the development of smart cities was selected as the output. Results: According to the results of the ranking, 33 (26%) cities are efficient, while 94 (74%) cities are inefficient. The most efficient cities are Korčula, Split, Pazin, Rijeka and Dubrovnik, while the most inefficient cities are Skradin, Petrinja, Bakar, Komiža, Glina and Kutina. Conclusion: By identifying the dimensions that have the greatest impact on the efficiency of smart cities, DMUs gain valuable information about the position of an individual city compared to other cities. Providing an overview of existing efficiency levels and suggesting improvement measures enables targeted changes towards efficiency.

Point estimates in survey sampling only provide a single value for the parameter being studied and are consequently vulnerable to changes caused by sampling error. In order to cope with ambiguity, indeterminacy, and uncertainty in data, Florentin Smarandache’s neutrosophic technique, which generates interval estimates with high probability, offers a helpful solution. To estimate the neutrosophic population mean of the studied variable, this research provides new neutrosophic factor type exponential estimators using well-known neutrosophic auxiliary parameters. For the first-degree of approximation, the study derives the bias and Mean Squared Error (MSE) of the proposed estimators. Characterising constants have neutrosophic optimal values, and for these optimum values, the least value of the neutrosophic MSE is obtained. Notably, the proposed neutrosophic estimators outperform the corresponding adapted classical estimators since their estimated interval falls under the minimal MSE and lies within the estimated interval of the proposed neutrosophic estimators. The theoretical results are supported by empirical data from real data sets acquired by the “Ministry of Earth Sciences” and the “India Meteorological Department (IMD), Pune, India,” as well as simulated data sets produced via Neutrosophic Normal Distribution. The estimator with the lowest MSE is suggested for practical applications across many domains, providing greater accuracy and reliability in parameter estimation when utilising the neutrosophic methodology.

It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance applications when data are scarce. We show that the canonical MPL method uses the mean of order statistics, and we propose to use the median or the mode instead. We show that the MPL estimators proposed are consistent and asymptotically normal. In a simulation study, we compare the finite sample performance of the proposed estimators with that of the original MPL and the inversion method estimators based on Kendall's tau and Spearman's rho. In our results, the modified MPL estimators, especially the one based on the mode of the order statistics, have a better finite sample performance both in terms of bias and mean square error. An application to general insurance data shows that the level of dependence estimated between different products can vary substantially with the estimation method used. [ABSTRACT FROM AUTHOR]

In this article, an estimator of population variance using multi-auxiliary information has been proposed. It is seen that under certain conditions, the proposed estimator is less biased and more efficiency than existing estimators. Theoretical results are supported numerically. Moreover, a simulation study also has been made. [ABSTRACT FROM AUTHOR]

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Randomized response (RR) technique using the Negative Binomial distribution was proposed by Hussain et al. It showed numerically that the method performs better than some of the existing methods including Kuk. However, the method of comparison of the efficiencies used by them was not fair. A more appropriate method of comparison is, therefore, proposed in this article and it is found that Kuk’s RR is better than the Hussain et al.’s in terms of execution and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we develop estimators of the finite population variance using the information on the study variable’s first and second raw moments under stratified random sampling. In addition, both combined and separate estimators are also developed for estimating the population variance using supplementary information on a study variable and one or two auxiliary variables. The proposed estimators are compared with the existing estimators in terms of absolute bias and relative efficiency. Based on simulated and empirical studies, it is found that the proposed estimators may outperform the existing estimators. [ABSTRACT FROM AUTHOR]