This study is motivated by the importance of the aforementioned variable selection issue.
Due to the presence of censoring, analyzing time-to-event data with high-dimensional covariates and recognizing efficient covariates in terms of predictive power of survival is more challenging. These methods involve different estimation methods and assumptions such as the Cox proportional hazard model, accelerated failure time, Buckley-James estimator, random survival forests, additive risk models, weighted least squares, or classification and regression tree (CART). Also, recently developed methods for identifying variable efficiency may operate faster, but the robustness is not consistent. Most of the traditional variable selection methods such as Akaike information criterion (AIC) or Bayesian information criterion (BIC) involve computational algorithms in a class of nondeterministic polynomial-time (NP-hard) and computational cost, making these procedures infeasible. Complex, high-dimensional, and censored time-to-event data provide an excellent chance for manufacturers to reduce costs, improve efficiency, and ultimately improve the quality of their products by detecting failure causes faster. In reliability analysis, failure time is determined by the variables contributing to products’ failure time. Access to valuable data for sophisticated analytics can substantially improve the management decision making process. Quality dimensions of products and services, defined by quality experts or perceived by customers, are summarized as performance, availability, reliability, maintainability, durability, serviceability, conformance, warranty, and aesthetics and reputation. The opportunity for manufacturing and services in the era of data is to analyze their performance to enhance the quality. As an analytical approach, decision making is the process of finding the best option from all feasible alternatives. As stated in a broad survey, advanced analytics are among the most popular techniques used in high-dimensional and massive data analysis and decision making process. It has been suggested that business should discover new ways to collect and use data every day and develop the ability to interpret data to increase the performance of their decision makers.
High dimensional data analysis methods professional#
In many professional areas and activities, as well as manufacturing and services, decision making is increasingly based on the type and size of data, as well as analytic methods, rather than on experience and intuition.
If data is compiled and processed correctly, it can enable informed decision making. In this situation, variable selection techniques could be used to determine a subset of variables that are significantly more valuable to analyze high-dimensional time-to-event datasets. Unlike traditional datasets with few explanatory variables, the analysis of datasets with a high number of variables requires different approaches. This massive amount of data is increasingly accessible from various sources such as transaction-based information, information-sensing devices, remote sensing technologies, machines and logistics statistics, wireless sensor networks, and analytics in quality engineering, manufacturing, service operations, and many other segments. By the advent of modern data collection technologies, a huge amount of this type of data includes high-dimensional covariates. Time-to-event data such as failure or survival times have been extensively studied in reliability engineering. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations.
The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability.
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