Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model typically evaluated using test sets or periodic manual quality assessments, both which require laborious labeling. Automated prediction techniques aim to mitigate this burden, but potential inaccuracy lack trust in their predictions has prevented widespread ad...

Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble direct interval estimation and conformal methods. An important issue is calibration of these methods: generated should a predefined coverage level, without being overly conservative. In this work, we review above four classes from concept...

Accurate forecasting is one of the fundamental focuses in literature econometric time-series. Often practitioners and policymakers want to predict outcomes an entire time horizon future instead just a single k-step ahead prediction. These series, apart from their own possible nonlinear dependence, are often also influenced by many external predictors. In this article, we construct prediction in...

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the has been followed by different tools like value logarithm likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian criterion. Moreover, likelihood ratio test and empirical posterior probabilit...

Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and probit regression in the prediction of Management's overconfident at pre...