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This study aims to introduce four modified linear estimators for the right-censored high-dimensional data. Obviously, data of interest involves two important problems to be solved that are censorship and high dimensionality. This paper can be distinguished from other studies in the literature with that it achieves to handle these two problems simultaneously. The main contribution of the paper is merging weightedridge method with the imputation techniques to obtain more efficient estimators than its alternatives. To solve the censorship problem, four imputation techniques are considered based on machine learning algorithms kNN, sliding-windows, regression and support vector machines. The high-dimensionality problem is handled by the weighted ridge approach which provides estimator with less risk than its alternatives because it detects the covariates with a weak contribution via the post-selection procedure. To show the empirical performance of the introduced estimators, a simulation study is made and comparative results are presented. Results show that kNN and regression imputation basis WR esitmators show satisfying performances on estimation of the high-dimensional right-censored model. [ABSTRACT FROM AUTHOR]

This paper proposes methodological procedures to be used by the accounting, organizational and managerial researchers and executives to ascertain the criticality of the variables and the processes in the measurement of management control system. We have restricted the validation of proposed methods to the extraction of critical success factors (CSF) in this study. We have also provided a numerical illustration and tested our methodological procedures using a dataset of an empirical study conducted for the purpose of ascertaining the CSFs. The proposed methods can be used by the researchers in accounting, organizational information systems, economics, and business and also in other relevant disciplines of organizational sciences. The main contribution of this paper is the extension of Rockart's work [33] on critical success factors. We have extended the theory of CSF beyond the initially suggested domain of information into management control system decision making. The methodological procedures developed by us are expected to enrich the literature of analytical and empirical studies in accounting and organizational areas where it can prove helpful in understanding the criticality of individual variables, processes, methods or success factors. [ABSTRACT FROM AUTHOR]