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Their study introduces a three stage DEA framework that employs random forest as a powerful ensemble method for variable selection to search most influential environmental variables. In management, economics and finance, regression analysis is largely used. It applies earnings management-based models, based on financial ratios and earnings management variables, to a sample of 6000 French small and medium-size enterprises, then compares the classification rates obtained by these models with a model based solely on financial ratios. This approach combines the standard quantile regression analysis with nonparametric estimations and allows examining how different quantiles of natural disasters affect different quantiles of GDP growth. [Extracted from the article]

An introduction is presented in which the editors discuss risk management, data envelopment analysis (DEA) and peer-to-peer (P2P) lending.

Credit rating models are widely used by banking institutions to assess the creditworthiness of credit applicants and to estimate the probability of default. Several pattern classification algorithms are used for the development of such models. In contrast to other pattern classification tasks, however, credit rating models are not only expected to provide accurate predictions, but also to make clear economic sense. Within this context, the estimated probability of default is often required to be a monotone function of the independent variables. Most machine learning techniques do not take this requirement into account. In this paper, monotonicity hints are used to address this issue within the modeling framework of support vector machines (SVM), which have become increasingly popular in this field. Non-linear SVM credit rating models are developed with linear programming, taking into account the monotonicity requirement. The obtained results indicate that the introduction of monotonicity hints improves the predictive ability of the models. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) are one of the most popular methodologies for the design of pattern classification systems with sound theoretical foundations and high generalizing performance. The SVM framework focuses on linear and nonlinear models that maximize the separating margin between objects belonging in different classes. This paper extends the SVM modeling context toward the development of additive models that combine the simplicity and transparency/interpretability of linear classifiers with the generalizing performance of nonlinear models. Experimental results are also presented on the performance of the new methodology over existing SVM techniques. [ABSTRACT FROM AUTHOR]

The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models. [ABSTRACT FROM AUTHOR]

Classification problems involve the assignment of a discrete set of alternatives described over some criteria into predefined groups. This paper is focused on the case where the groups are defined in an ordinal way. The methodology proposed in the paper is based on the multicriteria decision aid (MCDA) approach and the outranking relations theory. A new approach is presented to determine the parameters of an outranking relation classification model using a training sample of alternatives. The proposed methodology is extensively validated in terms of its classification performance through a comprehensive Monte Carlo simulation. The results of the methodology are compared to linear, quadratic and logit analysis. [ABSTRACT FROM AUTHOR]

Real estate ecosystems are the result of symbiotic relationships between different types of real estate, including residential, commercial, or industrial properties. Maintaining a balance among these categories is essential for these ecosystems' ability to function properly. Real estate currently is largely a product of urban expansion caused by population increases and distances from primary-sector industries. The unplanned growth of urban areas can have profound consequences for local residents' health and the environment. However, when well structured, urbanization can have a positive effect on societies' well-being. The relationship between this expansion and its impact on real estate ecosystems is an extremely complex, subjective topic, so decision makers cannot apply just one solution when making plans. This study develops a multicriteria analysis model to support decision making processes, which provides a structured understanding of urban expansion effects on real estate ecosystems and deals with the inherent subjectivity and complexity of this decision problem. Cognitive mapping and decision-making trial and evaluation laboratory (DEMATEL) technique were combined in a neutrosophic environment to incorporate uncertainty and produce a more realistic analysis system, thereby overcoming various limitations of previous research on urban expansion (e.g. , unclear way by which relevant factors/criteria are identified in most cases, and lack of analyses dealing with the dynamics of their causal relationships). Decision makers with professional experience in relevant areas collaborated to provide essential input used to create the proposed model. External experts validated the results, confirming the potential of our analysis system for real-life applications. • This study provides a holistic analysis of urban expansion, fostering its nuanced real-world understanding. • The process-oriented approach followed overcomes limitations inherent in purely statistical methods. • Cognitive mapping enhances the understanding of urban-expansion effects. • Integration of cognitive mapping, neutrosophic logic, and DEMATEL forms a unique analytical approach to urban expansion. [ABSTRACT FROM AUTHOR]

Family business growth is crucial not only for the long-term sustainability of this type of firm but also for economic growth as a whole. However, family businesses face internal and external obstacles that hamper their healthy, sustainable growth. Analyses of family business growth are critical to overcoming these barriers. By combining fuzzy cognitive mapping techniques and the system dynamics approach, this study sought to create an analysis model that allows for a more holistic perspective on determinants of family business growth, their cause-and-effect relationships, and thus their long-term behavior. The model developed is a complete, transparent, and realistic tool that enables decision makers to make better and more informed decisions. This study was grounded in the insights provided by a panel of experts in family business, who shared their knowledge and experience over the course of two group sessions. The results of the static and dynamic analyses carried out were validated by the head of department and a board member of the Southern Business Support Center of Portugal's Institute for the Support of Small and Medium-sized Enterprises and Innovation (IAPMEI). This research's contributions are also discussed, and suggestions are made for future research. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports within the issue on topics including measurement of bank branches' efficiency, examination of credit unions' dynamic operation, and an integrated method for mortgage choice and pension savings planning.

This study uses fixed-effect regressions estimated with heteroskedasticity-consistent standard errors to investigate the effect of international diversification on corporate cash holding behavior of French-listed firms during economic downturns. The findings show that internationally diversified firms are less inclined to save cash out of their cash flows than their undiversified counterparts. However, during economic downturns, the relationship shifts and shows that international diversification is positively associated with the propensity of firms to save cash out of their cash flows. The negative relationship between international diversification and the propensity of firms to save cash out of their cash flows suggests that risk-reducing effects coupled with easy access to external finance prevail over the high agency costs and information asymmetry associated with international companies. However, during economic slumps, this relationship becomes positive, highlighting a significant influence of the financial crisis on internationally diversified firms relative to their stand-alone counterparts. Thus, this study should provide useful insights for academics, practitioners as well as financial regulators. [ABSTRACT FROM AUTHOR]

Our purpose in this article is to investigate the benefits of introducing quantitative strategies for the estimation of portfolio variance–covariance matrices, expecting that the stylized facts of asset returns and their economic impact will be effectively captured. More specifically, we are dealing with the process of portfolio optimization with rebalancing for ETFs portfolios, in a time-varying volatility environment. The aim of the analysis is to construct optimal portfolios, based on the econometric modelling and calculation of return covariances. Also, our target is to infer critical comparative insights, as far as the application of three popular quantitative frames: (a) the sample covariance or equal weighting model, (b) the EWMA model, and (c) the GARCH (1,1) model. The validity of the attempt is verified through an illustrative empirical testing procedure on an actively traded low-volatility momentum mutual fund-of-ETFs, consisting of a well-diversified investment universe of 150 ETFs. Additionally, we co-assess a set of non-convex investment policy restrictions, such as buy-in thresholds and compliance norms, modelling the corresponding portfolio selection process as a mixed-integer optimization problem. The qualitative and technical conclusions obtained, document superior out-of-sample returns for the portfolios constructed by means of the EWMA and GARCH (1,1) models. Moreover, other findings that confirm and expand the existing underlying research, are also reported. [ABSTRACT FROM AUTHOR]

Exponential increase in economic activity and high levels of consumption have hindered long-term planning and made the sustainable management across different areas more difficult. Given this context, decision problems have been complicated by an ever-greater degree of complexity, forcing decision makers to seek out new approaches and methodologies that facilitate the processes that support decision-making. MCDM and MCDA are now well-known acronyms for multiple-criteria decision-making and multiple-criteria decision analysis, respectively. Due to MCDM/A methods' exponential growth over the past few years, this special issue sought to bring together recent developments and methodological contributions within MCDM/A for economic development, social cohesion and environmental sustainability. All contributions relate to the 2030 Agenda for Sustainable Development, and were selected based on the way they relate to the Agenda's sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

The recent transformation of football clubs to businesses and the challenges posed by this transformation motivate us to study the financial, business, and sports performance of French football clubs. We propose a two-stage method that can be applied to other settings, especially when there exist sample size and theoretical/model specification issues: first, Multicriteria Analysis is used to rank clubs on their financial and business performance dimensions; second, these rankings and the league standing (capturing sports performance) are used to assess the interrelationships of the different dimensions by means of a Partial Least Squares Structural Equation Modeling Approach. We find an amphidromous positive relationship between business performance and sports performance, and a one-way inverse relationship where financial performance affects sports performance. Put simply, more revenues affect sports achievements positively and these in turn impact positively on revenues in a virtuous cycle. The higher revenues do not aid financial performance given a race for success that can be possibly augmented by stakeholder myopia: the inherent to the sport pursuit of short term objectives to the detriment of long term sustainability. Consequently, the role of regulators (national authorities, UEFA Financial Fair Play) as custodians, is ever more important in protecting clubs from financial distress. [ABSTRACT FROM AUTHOR]

A preface for a 2007 issue of "Annals of Operations Research" is presented.

The paper presents a bibliographic analysis of multicriteria sorting and clustering methods, highlighting influential studies in these areas, identifying the status in the field, and pointing recent research developments and trends. A wide range of decision-making problems in various areas of engineering and management require the assignment of a set of decision options (alternatives) to categories/classes or groups. [Extracted from the article]

The continuous growth of hospital costs has driven governments in many countries to seek ways to improve their efficiency. In Greece, this has consistently been a major issue for almost two decades, as efficiency assessment and monitoring systems are lacking. In response to this need, the evaluation of the National Health System hospitals' efficiency level is a precondition for planning, implementing and monitoring any promising reform. In this paper, a non-parametric modeling approach is employed to assess and analyze the efficiency of 87 Greek public hospitals over the period 2005-2009, using data envelopment analysis. The operational and economic aspects of the hospitals' operation are considered on the basis of their service/case mix and cost structure. We also investigate the efficiency trends over time with the Malmquist index and a second stage regression analysis is performed to explain the operational and economic efficiency results in terms of the hospitals' operating characteristics and the environment in which they operate. [ABSTRACT FROM AUTHOR]

Given the increasingly significant role of small and medium-sized enterprises (SMEs) in the global economy and the ever more competitive markets in which these companies operate, SMEs’ ability to adopt artificial intelligence (AI) technologies is of utmost importance. Due to constantly evolving social, environmental, and technological scenarios, the managers of these firms must increasingly focus on incorporating new tools such as AI into SME operations in order to enjoy their benefits. However, the subjectivity and complexity of this adaptation process makes integrated analyses of key factors challenging. The present study sought to develop a multi-criteria decision-support system that applies cognitive mapping and the decision-making trial and evaluation laboratory technique in a neutrosophic context. The main objective is to overcome the limitations of previous studies and models by structuring the decision problem and identifying and understanding which factors should be central to adaptation initiative analyses. A panel of experts in AI were recruited to facilitate the construction of an analysis system that takes into account indeterminacy in decision-making processes. The results were validated by both the panel members and project managers at COTEC Portugal—a leading think-and-action network that seeks to advance technology diffusion and business innovation cooperation. The proposed system’s practical implications and benefits are also analyzed. [ABSTRACT FROM AUTHOR]

Corporate economic performance and its association with carbon emissions has been the subject of extensive research within the last few decades. The present study examines the economic performance of green firms as reflected in the Financial Times Stock Exchange Environmental Opportunities Index Series (FTSE EO) and its association with carbon emissions, incorporating the role of two more indices, namely the Baltic Clean Tanker Index (BAIT) and EUR/USD exchange rate. The methodology employed is the Bayesian Vector Autoregression Model (BVAR). Our findings confirm the interlinkages among the variables studied. More specifically, based on impulse response analysis, the direction of causality is two-way. The response of carbon emissions to a shock in the other variables is negative for the case of the EUR/USD exchange rate and Baltic Clean Tanker Index (BAIT) (an index representing the conventional energy sources), while it is positive for a shock in the FTSE EO. However, the most important finding is the return to the steady state after nine or ten periods. This result is very significant since the global environmental agreements along with the global economic conditions and the energy resources may well lead to limitations in carbon emissions within a framework of a well-organized and targeted climate change mitigation strategy. [ABSTRACT FROM AUTHOR]

Highlights: [•] Combination of option-based model with accounting data for credit risk model. [•] Application of market model to non-listed firms. [•] Use of a novel additive support vector machines model. [ABSTRACT FROM AUTHOR]

Automated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression approaches. This study presents a comparative analysis about the performance of parametric and non-parametric regression techniques for developing reliable automated valuation models for residential properties. Different approaches are explored to incorporate spatial effects into the valuation process, covering both global and locally weighted models. The analysis is based on a large sample of properties from Greece during the period 2012–2016. The results demonstrate that linear regression models developed with a weighted spatial (local) scheme provide the best results, outperforming machine learning approaches and models that do not consider spatial effects. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to solve feature subset selection problems using an Ant Colony Optimization (ACO) algorithm. ACO is one of the important naturally inspired intelligent techniques. It is based on the foraging behavior of real ants in nature. The proposed ACO is combined with a number of nearest neighbor classifiers. The resulting ACO algorithm is applied to classify credit risk using data belonging to 1,411 firms obtained from a leading Greek commercial bank. The objective is to classify subject firms into several groups representing different levels of credit risk. The results of the proposed algorithm are compared with those of others including SVM, CART, and with two other metaheuristic algorithms using tabu search and genetic algorithms, both of which use nearest neighbor classifiers in the classification phase. The results suggest that the proposed method is more accurate than others that have been tested in classifying credit risk. [ABSTRACT FROM AUTHOR]

Previous literature is rather inconclusive concerning the impact of state ownership on banks. We report that its overall impact is not monotonic as it has so far been implicitly assumed, and that it depends on a contemporaneous conflicting impact on risk and financial performance. This suggests the existence of an optimal level, which we investigate by comparing the relative "overall performance" and efficiency of the institutions. We show that a minimal presence, as opposed to no state ownership can improve performance and efficiency, reduce the likelihood of a bailout, while it is less costly compared to capital injections. [ABSTRACT FROM AUTHOR]

Robust optimization (RO) models have attracted a lot of interest in the area of portfolio selection. RO extends the framework of traditional portfolio optimization models, incorporating uncertainty through a formal and analytical approach into the modeling process. Although several RO models have been proposed in the literature, comprehensive empirical assessments of their performance are rather lacking. The objective of this study is to fill in this gap in the literature. To this end, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005–2020. For the analysis, two different robust versions of the mean–variance model are considered, together with robust models for conditional value-at-risk and the Omega ratio. The robust versions are compared against the nominal ones through various portfolio performance metrics, focusing on out-of-sample results. [ABSTRACT FROM AUTHOR]

Sustainable banking is an issue that has been growing in importance around the world since the 2008 global crisis. Because the banking system is a major economic and financial intermediary, it has a significant impact on diverse stakeholders. In this context, understanding the determinants of long-term survival of the banking industry appears to be important to ensuring sustainable banking. Based on a constructivist stance, this study sought to identify sustainability strategies in the banking industry through a combination of fuzzy cognitive mapping (FCM) and the system dynamics (SD) approach. The main goal is to develop an easily understood model of sustainable banking to help decision makers analyze the dynamics of the cause-and-effect relationships between determinants of sustainable banking and project long-term scenarios. The insights obtained were derived from two group sessions with a panel of decision makers who deal with banking processes on a daily basis and were validated by the Director of the technical unit for sustainability of Millennium BCP, one of the largest banking corporations in Portugal. Static and dynamic analyses were also carried out, the results of which support the study's main conclusions and reinforce the relevance of the proposed dual methodology. [ABSTRACT FROM AUTHOR]

Quality of life and well-being is a topic of major importance for public policy making and urban planning decisions. However, these concepts entail complex multiple socio-economic aspects, and are ill-defined. Quantitative assessments of quality of life based on composite indicators have become quite popular in academic research and practice. This study presents the application of an approach that combines ideas from multiple criteria decision making and efficiency analysis to the assessment of quality of life at the municipality level. A dataset involving French municipalities in 2012 is used for the analysis based on a rich set of key performance indicators that cover different aspects of quality of life conditions for citizens. The relationship of quality of life performance with the financial strength of the municipalities is also explored. [ABSTRACT FROM AUTHOR]

Based on what we know today, there is a strong linkage between solar activity and the energy sector. In this work, we study the linkage between extreme solar events and the US energy sector, utilizing a sectoral supply chain dataset that spans the period 1998–2014. To do so, we propose a novel methodological approach by linking all the sectors of the economy’s supply chain through a network-theoretic production-based framework. We estimate the network system using a global vector autoregressive model that incorporates dominant entities. Additionally, to capture the indirect spillover impact of extreme solar events on the rest of the sectors in the US economy, we develop, for the first time in the literature, the indirect global impulse response functions. We find that solar events affect directly the energy sector and, indirectly, most of the other sectors of the US economy. More precisely, solar phenomena indirectly provoke positive and significant responses in manufacturing, transportation, information and communication, finance and insurance, real estate, and business activities, through the energy sector. Conversely, agriculture, forestry, fishing, and mining sectors exhibit negative and significant responses. Construction, accommodation and food service activities, and wholesale and retail trade sectors do not show any significant response. These results give credence to the impact of solar events on the US sectoral economy, directly and indirectly, through the proposed IGIRFs. [ABSTRACT FROM AUTHOR]

We attempt to establish an integrated portfolio optimization business framework, in order to bridge the underlying gap between the complex mathematical theory of multiobjective mathematical programming and asset management practice. Our aim is to assist practitioners and portfolio managers in formulating successful investment strategies, by providing them with an effective decision support tool. In particular, we propose a multiobjective portfolio model, able to support the simultaneous optimization of multiple investment objectives. We also manage to integrate a set of sophisticated real-world non-convex investment policy limitations, such as the cardinality constraints, the buy-in thresholds, the transaction costs, along with particular normative rules. The underlying investment management rationale of the proposed managerial protocol is displayed through an illustrative business flowchart, while we also provide an analytical step-by-step portfolio management business routine. The validity of the model is verified through an extended empirical testing application on the Eurostoxx 50. According to the results, a sufficient number of efficient or Pareto optimal portfolios produced by the model, appear to possess superior out-of-sample returns with respect to the underlying benchmark. [ABSTRACT FROM AUTHOR]

Environmental policy measures to reduce greenhouse gases emission to mitigate climate change are common practice in many developed countries, encompassing both technology-push measures and demand-pull measures. The effectiveness of such policies continues to need investigation, especially when shortsighted measures lead to suboptimal investments in the absence of a credible government commitment. The aim of this paper is to assess the effectiveness of a comprehensive strategy for renewable energy sources in a dynamic stochastic general equilibrium model estimated for the Euro area using Bayesian estimation techniques, which includes carbon tax and subsidy measures. To this end, we compare the cost-effectiveness of technology-push measures and demand-pull measures. Our findings show that the environmental policy based on technology-push measures may produce better dynamic effects than demand-pull measures based on a subsidy policy of equal monetary amount. In fact, RES price parity is estimated to occur sooner by implementing technology-push measures than demand-pull measures. [ABSTRACT FROM AUTHOR]

This paper evaluates the energy efficiency of EU countries over the period 2000-2010. At the first stage, data envelopment analysis (DEA) is used, combining multiple energy consumption data and economic outputs. The efficiency estimates obtained from the analysis are evaluated in a second stage through a multiple criteria decision aiding methodology (MCDA). The proposed non-parametric approach combining DEA with MCDA enables modeling of the problem in an integrated manner, not only providing energy efficiency estimates but also supporting the analysis of the main contributing factors, as well as the development of a benchmarking model for energy efficiency evaluation at the country level. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increase in the number and value of securities class actions (SCAs), attracting the attention of various stakeholders such as investors, managers, policy makers, lawyers, etc. The present study extends the literature, by investigating for the first time the development of a classification model to forecast SCAs filed against US banks. Our results show that the proposed multicriteria decision aid model achieves a satisfactory accuracy, by classifying correct around 80 % of the banks in an out-of-sample testing. We obtain similar results when we use a walk-forward approach, instead of a tenfold cross-validation technique, for the estimation and testing of the model. Further analysis indicates that the classification accuracies can improve further by the inclusion of a corporate governance indicator that relates to executive and director compensation and ownership. [ABSTRACT FROM AUTHOR]

Abstract: Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000–2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables, we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios. [Copyright &y& Elsevier]

Cluster analysis is an important tool for data exploration and it has been applied in a wide variety of fields like engineering, economics, computer sciences, life and medical sciences, earth sciences and social sciences. The typical cluster analysis consists of four steps (i.e. feature selection or extraction, clustering algorithm design or selection, cluster validation and results interpretation) with feedback pathway. These steps are closely related to each other and affect the derived clusters. In this paper, a new metaheuristic algorithm is proposed for cluster analysis. This algorithm uses an Ant Colony Optimization to feature selection step and a Greedy Randomized Adaptive Search Procedure to clustering algorithm design step. The proposed algorithm has been applied with very good results to many data sets. [ABSTRACT FROM AUTHOR]

Abstract: Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Such models ought to be as accurate as possible. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. This is known as the feature selection problem in the machine learning/data mining field. In financial decisions, feature selection is often based on the subjective judgment of the experts. Nevertheless, automated feature selection algorithms could be of great help to the decision-makers providing the means to explore efficiently the solution space. This study uses two nature-inspired methods, namely ant colony optimization and particle swarm optimization, for this problem. The modelling context is developed and the performance of the methods is tested in two financial classification tasks, involving credit risk assessment and audit qualifications. [Copyright &y& Elsevier]

Abstract: The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability. [Copyright &y& Elsevier]

Although many empirical studies have focused on R & D performance models for markets globally, the available financial information for R & D expenditure is limited. In other words, can we assume that the reported accounting information for R & D investment is adequate and valuable? This study empirically investigates the effect of R & D reported information on the value relevance of the accounting information of firms' financial statements. Specifically, using Ohlson's equation, it is examined whether changes in stock prices are explained better when R & D factors are included in models, in conjunction with changes in book value and abnormal earnings. We focus on listed firms on the Athens Stock Exchange in order to explore whether R & D expenses are value relevant, in a market which has been affected for a long period by the global economic crisis of 2007. In our findings, we observe that the reported R & D expenses do not have any significant influence on the investors' choices, in contrast to expectations based on the prior literature. Moreover, the panel data analysis employed in the paper overcomes common methodological problems (such as autocorrelation, multicollinearity, and heteroscedasticity) and allows the estimation of unbiased and efficient estimators. [ABSTRACT FROM AUTHOR]