In this paper, we propose a two-step less volatile value-at-risk (LVaR) estimation using the generalized nearly-isotonic regression (GNIR) model. The first step of our LVaR estimation is to produce a VaR sequence under the generalized autoregressive conditional heteroskedasticity (GARCH)...

This study develops a novel distribution-free maximum-likelihood estimator and formulates it for linear regression and binary response models. The estimator is consistent and asymptotically normally distributed (at rate N^(-1/2)). The Monte Carlo simulation results for the linear and binary...

When constructing parametric models to predict the cost of future claims, several important details have to be taken into account: (i) models should be designed to accommodate deductibles, policy limits, and coinsurance factors, (ii) parameters should be estimated robustly to control the...

Two election forensics methods provide estimates of the magnitude of election fraud: Bayesian version of the finite mixture model developed by Walter Mebane and his students, and nonparametric approach based on vote-turnout histograms proposed by Sergey Shpilkin. While Mebane's approach offers...

Robust Estimation of Loss Models for Lognormal Insurance Payment Severity Data Chudamani Poudyal1Department of Statistics and Data Science University of Central Florida. The primary objective of this scholarly work is to develop two estimation procedures –maximum likelihood estimator(MLE) and...

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are satisfied. We show that a conditional Gaussian...

A new tabu search algorithm is proposed for the maximum score estimator computation, where the focus is on large sample size and/or large number of parameters. The proposed algorithm shares the same solution representation with the Hyperplanes Intersection Simulated Annealing (HISA) previously...

This paper introduces a flexible local projection that generalises the model by Jorda (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first...

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric non-stationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to...