Entrenchment in publicly traded family firms: Evidence from the S&P 500

Family involvement in corporate governance through ownership, management, and board membership presents a unique dilemma for understanding the strategic impetus and costs of entrenchment decisions. The presence of shared family ties and the family-centered goals of firm principals call to question the applicability of extant agency arguments regarding the nature and antecedents of managerial entrenchment. Exploring this, we develop and test a model of family firm-specific determinants (i.e., family ownership and family's involvement in management and governance) of entrenchment in publicly traded firms by drawing upon principal-principal agency theory. Findings of the empirical analysis of family owned S&P 500 firms suggest family firms are motivated to entrench managers when doing so supports the pursuit of family-centric goals. However, the extent to which entrenchment supports such goals varies at different levels of family ownership.     

Particle swarm optimization based Liu-type estimator

In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.     

Testing equality of variances for several normal populations

In this study, we develop a test based on computational approach for the equality of variances of several normal populations. The proposed method is numerically compared with the existing methods. The numeric results demonstrate that the proposed method performs very well in terms of type I error rate and power of test. Furthermore we study the robustness of the tests by using simulation study when the underlying data are from t, exponential and uniform distributions. Finally we analyze a real dataset that motivated our study using the proposed test.     

New difference-based estimator of parameters in semiparametric regression models

The paper introduces a new difference-based Liu estimator β?_Ldiff=([Xtilde]′[Xtilde]+I)^?1([Xtilde]′[ytilde]+η β?_diff) of the regression parameters β in the semiparametric regression model, y=Xβ+f+?. Difference-based estimator, β?_diff=([Xtilde]′[Xtilde])^?1[Xtilde]′[ytilde] and difference-based Liu estimator are analysed and compared with respect to mean-squared error (mse) criterion. Finally, the performance of the new estimator is evaluated for a real data set. Monte Carlo simulation is given to show the improvement in the scalar mse of the estimator.     

A Parallel between Regret Theory and Outranking Methods for Multicriteria Decision Making Under Imprecise Information

Incorporation of the behavioral issues of the decision maker (DM) is among the aspects that each Multicriteria Decision Making (MCDM) method implicitly or explicitly takes into account. As postulated by regret theory, the feelings of regret and rejoice are among the behavioral issues associated with the entire decision making process. Within the context of MCDM, the DM may feel regret, when the chosen alternative is compared with another one having at least one better criterion value. PROMETHEE II is a widely known MCDM method that makes no explicit incorporation of regret attitude of the DM. In this paper, we elaborate on the applicability of regret theory to MCDM context. In particular, we investigate the findings of regret theory and explore the parallel between regret theory and PROMETHEE II method. Relying on the concepts of regret theory, we demonstrate how a decision that is made using a PROMETHEE II based outranking method conforms to the regret attitude of the DM.     

Gendering the Organization of Home‐based Work in Turkey: Classical versus Familial Patriarchy

This article examines the link between industrial homework and family dynamics in Turkey by asking how and why varied patterns emerge in the organization of industrial homework. I develop a framework for the analysis of gendered organization of industrial homework by comparing labour practices in extended family and nuclear family households. In extended family households, subcontracts are made between industrial enterprises and male family members. Women carry out piece‐rated work at home under the supervision of male family members and have very little contact with people outside their family. In nuclear family households, subcontracts are made between women homeworkers and industrial enterprises or middle persons. Women work at residential streets and neighbourhood homework stores, as well as at home. They have control over their income as well as their working hours. To explain the divergent practices of industrial homework, I show that enterprises incorporate distinct patterns of patriarchy into the subcontracting arrangements through gendered organization of industrial homework.     

Efficiency of the generalized difference-based Liu estimators in semiparametric regression models with correlated errors

In this paper, a generalized difference-based estimator is introduced for the vector parameter β in the semiparametric regression model when the errors are correlated. A generalized difference-based Liu estimator is defined for the vector parameter β in the semiparametric regression model. Under the linear nonstochastic constraint Rβ=r, the generalized restricted difference-based Liu estimator is given. The risk function for the β?_GRD(η) associated with weighted balanced loss function is presented. The performance of the proposed estimators is evaluated by a simulated data set.     

Goodness of fit tests for Nakagami distribution based on smooth tests

ABSTRACT

Nakagami distribution is one of the most common distributions used to model positive valued and right skewed data. In this study, we interest goodness of fit problem for Nakagami distribution. Thus, we propose smooth tests for Nakagami distribution based on orthonormal functions. We also compare these tests with some classical goodness of fit tests such as Cramer–von Mises, Anderson–Darling, and Kolmogorov–Smirnov tests in respect to type-I error rates and powers of tests. Simulation study indicates that smooth tests give better results than these classical tests give in respect to almost all cases considered.