Optimizing Expert Rankings with Multiple Regression Analysis

The primary goal of this research is to establish a methodology that gives significant weight to an expert's qualifications and experience in the expertise ranking process. This methodology aims to enhance the effectiveness of expert identification by taking into account an expert's background and credentials, thus yielding more realistic expert rankings. To achieve this, we incorporate the details of an expert's qualifications and experience into the evaluation process by assigning assumed values, which are then integrated with their expertise level. These combined factors are subsequently utilized as inputs for a multiple regression analysis to generate an optimal ranking of experts. By emphasizing the significance of experience and qualifications in the ranking process, we can significantly improve the precision of our expert ranking mechanism. Our approach employs multiple regression analysis to identify the most suitable subject expert for user query transformation

Lifetime Assessment of Electrical Insulation

In this chapter, a review of the Weibull probability distribution, probability ranking, and the Weibull graphical estimation technique is presented. A review of single-stress and multiple-stress life models of electrical insulation is also introduced. The chapter also describes the graphical, linear and multiple linear regression techniques used in estimating the parameters of the aging models. The application of maximum likelihood estimation technique for estimating the parameters of combined life models of electrical insulation is illustrated

Corporate social responsibility performance and the cost of capital in BRICS countries. The problem of selectivity using environmental, social and governance scores.

The paper aims to test whether corporate social responsibility (CSR) performance affects the costs of debt, equity, and a weighted average of those two components in BRICS countries. Theoretically, a decline in the cost of capital is linked to a decrease in the firm risk. We measure CSR performance using the environmental, social, and governance (ESG) combined score from the Thomson Reuters EIKON database for non-financial enterprises between 2014 and 2019. A panel regression analysis has been run in order to test whether (1) the inclusion in the ESG combined ranking or (2) the level of the scores for ESG combines is linked to a decline in the cost of capital. Empirical evidence suggests that the level of the ESG combined score does not affect the firm's financial risk. Inclusion in the ESG combined index decreases the cost of equity and the average cost of capital instead. Firms that received an ESG combined score pay lower returns to investors

Ranking Median Regression: Learning to Order through Local Consensus

This article is devoted to the problem of predicting the value taken by a random permutation $\Sigma$, describing the preferences of an individual over a set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of an input/explanatory r.v. $X$ e.g. characteristics of the individual), when error is measured by the Kendall $\tau$ distance. In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\; (X_N, \Sigma_N)$. For this reason, this statistical learning problem is referred to as \textit{ranking median regression} here. Our contribution is twofold. We first propose a probabilistic theory of ranking median regression: the set of optimal elements is characterized, the performance of empirical risk minimizers is investigated in this context and situations where fast learning rates can be achieved are also exhibited. Next we introduce the concept of local consensus/median, in order to derive efficient methods for ranking median regression. The major advantage of this local learning approach lies in its close connection with the widely studied Kemeny aggregation problem. From an algorithmic perspective, this permits to build predictive rules for ranking median regression by implementing efficient techniques for (approximate) Kemeny median computations at a local level in a tractable manner. In particular, versions of $k$-nearest neighbor and tree-based methods, tailored to ranking median regression, are investigated. Accuracy of piecewise constant ranking median regression rules is studied under a specific smoothness assumption for $\Sigma$'s conditional distribution given $X$

Automatic Quality Estimation for ASR System Combination

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to insert at each position in the output transcriptions, some ROVER extensions rely on critical information such as confidence scores and other ASR decoder features. This information, which is not always available, highly depends on the decoding process and sometimes tends to over estimate the real quality of the recognized words. In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses. We first introduce an effective set of features to compensate for the absence of ASR decoder information. Then, we apply QE techniques to perform accurate hypothesis ranking at segment-level before starting the fusion process. The evaluation is carried out on two different tasks, in which we respectively combine hypotheses coming from independent ASR systems and multi-microphone recordings. In both tasks, it is assumed that the ASR decoder information is not available. The proposed approach significantly outperforms standard ROVER and it is competitive with two strong oracles that e xploit prior knowledge about the real quality of the hypotheses to be combined. Compared to standard ROVER, the abs olute WER improvements in the two evaluation scenarios range from 0.5% to 7.3%