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Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.

The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social, behavioral, environmental sciences, and econometrics. However, traditional least-squares-based techniques frequently used for panel data models are vulnerable to the adverse effects of the data contamination or outlying observations that may result in biased and inefficient estimates and misleading statistical inference. In this study, we propose a minimum density power divergence estimation procedure for panel data regression models with random effects to achieve robustness against outliers. The robustness, as well as the asymptotic properties of the proposed estimator, are rigorously established. The finite-sample properties of the proposed method are investigated through an extensive simulation study and an application to climate data in Oman. Our results demonstrate that the proposed estimator exhibits improved performance over some traditional and robust methods in the presence of data contamination., Comment: 28 pages, 1 figure

In this study, an ordered non-overlapping block bootstrap procedure has been proposed to obtain multi-step forecast regions for unrestricted vector autoregressive models. The proposed method is not based on either backward or forward representations, so it can be implemented to VARMA or VAR-GARCH models. Also, it is computationally more efficient than the existing techniques. Its finite sample performance is investigated by Monte Carlo experiments and two-real world examples. Our findings show that the proposed method is a good alternative to the available resampling methods and produces better results for long-term forecasting when the model is near non-stationary or near-cointegrated.

The panel data regression models have gained increasing attention in different areas of research including but not limited to econometrics, environmental sciences, epidemiology, behavioral and social sciences. However, the presence of outlying observations in panel data may often lead to biased and inefficient estimates of the model parameters resulting in unreliable inferences when the least squares (LS) method is applied. We propose extensions of the M-estimation approach with a data-driven selection of tuning parameters to achieve desirable level of robustness against outliers without loss of estimation efficiency. The consistency and asymptotic normality of the proposed estimators have also been proved under some mild regularity conditions. The finite sample properties of the existing and proposed robust estimators have been examined through an extensive simulation study and an application to macroeconomic data. Our findings reveal that the proposed methods often exhibits improved estimation and prediction performances in the presence of outliers and are consistent with the traditional LS method when there is no contamination.

In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved performance and, is computationally more efficient compared to conventional JaB method.

In different fields of applications including, but not limited to, behavioral, environmental, medical sciences, and econometrics, the use of panel data regression models has become increasingly popular as a general framework for making meaningful statistical inferences. However, when the ordinary least squares (OLS) method is used to estimate the model parameters, presence of outliers may significantly alter the adequacy of such models by producing biased and inefficient estimates. In this work, we propose a new, weighted likelihood based robust estimation procedure for linear panel data models with fixed and random effects. The finite sample performances of the proposed estimators have been illustrated through an extensive simulation study as well as with an application to blood pressure dataset. Our thorough study demonstrates that the proposed estimators show significantly better performances over the traditional methods in the presence of outliers and produce competitive results to the OLS based estimates when no outliers are present in the dataset.

In this study, we propose sufficient time series bootstrap methods that achieve better results than conventional non-overlapping block bootstrap, but with less computing time and lower standard errors of estimation. Also, we propose using a new technique using ordered bootstrapped blocks, to better preserve the dependency structure of the original data. The performance of the proposed methods are compared in a simulation study for MA(2) and AR(2) processes and in an example. The results show that our methods are good competitors that often exhibit improved performance over the conventional block methods.

This study presents two interval-valued time series approaches to construct multivariate multi-step ahead joint forecast regions based on two bootstrap algorithms. The first approach is based on fitting a dynamic bivariate system via a VAR process for minimum and maximum of the interval while the second approach applies for mid-points and half-ranges of interval-valued time series. As a novel perspective, we adopt two bootstrap techniques into the proposed interval-valued time series approaches to obtain joint forecast regions of the lower/upper bounds of the intervals. The forecasting performances of the proposed approaches are evaluated by extensive Monte Carlo simulations and two real-world examples: (i) monthly S&P 500 stock indices; (ii) monthly USD/SEK exchange rates. Our results demonstrate that the proposed approaches are capable of producing valid multivariate forecast regions for interval-valued time series.

In this paper, we propose a new bootstrap algorithm to obtain prediction intervals for generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) process which can be applied to construct prediction intervals for future returns and volatilities. The advantages of the proposed method are twofold: it (a) often exhibits improved performance and (b) is computationally more efficient compared to other available resampling methods. The superiority of this method over the other resampling method-based prediction intervals is explained with Spearman’s rank correlation coefficient. The finite sample properties of the proposed method are also illustrated by an extensive simulation study and a real-world example.

In this study, we propose an approach based on the residual-based bootstrap method to obtain valid prediction intervals using monthly, short-term (three-months) and mid-term (six-months) drought observations. The effects of North Atlantic and Arctic Oscillation indexes on the constructed prediction intervals are also examined. Performance of the proposed approach is evaluated for the Palmer Drought Severity Index (PDSI) obtained from Konya closed basin located in Central Anatolia, Turkey. The finite sample properties of the proposed method are further illustrated by an extensive simulation study. Our results revealed that the proposed approach is capable of producing valid prediction intervals for future PDSI values. (C) 2018 Elsevier B.V. All rights reserved.