RELIABILITY OF THE GPS CARRIER-PHASE FIX SOLUTION UNDER HARSH CONDITION

Once the unknown integer ambiguity values are resolved, the GPS carrier phase observation will be transformed into a millimeter-level precision measurement. However, GPS observation are prone to a variety of errors, making it a biased measurement. There are two components in identifying integer ambiguities: estimation and validation. The estimation procedure aims to determine the ambiguity's integer values, and the validation step checks whether the estimated integer value is acceptable. Even though the theory and procedures for ambiguity estimates are well known, the topic of ambiguity validation is still being researched. The dependability of computed coordinates will be reduced if a false fixed solution emerges from an incorrectly estimated ambiguity integer value. In this study, the reliability of the fixed solution obtained by using several base stations in GPS positioning was investigated, and the coordinates received from these bases were compared. In a conclusion, quality control measures such as employing several base stations will improve the carrier phase measurement's accuracy.

NEW METHOD FOR ESTIMATION OF MULTI-HARMONIC POWER SIGNAL PARAMETERS

A systematic analytical procedure for simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves was proposed in this paper. In order to reduce complexity in the calculation of unknown parameters, a completely new reduced analytical expression is derived, which enabled fast and precise estimation with a small numerical error. Individual sinusoidal components stand out from the input complex-harmonic signal with the filter with a finite-impulse response (FIR) comb filters. The algorithm that is proposed in the operation is based on the application of partial derivate of the processed and filtered input signal, after which it is performed weighted estimation procedure to better estimate the values size of the fundamental frequency, amplitude and the multi-sinusoid signal phase. The proposed algorithm can be used in the signal reconstruction and estimation procedures, spectral processing, in procedures for the identification of the system that is observed, as well as other important signal processing areas. Through the simulation check, the effectiveness of the proposed algorithm was assessed, which confirmed its high performance.

Towards Parameter Identification of a Behavioral Model from a Virtual Reality Experiment

In this paper, we investigate the calibration of a mathematical model describing different behaviors occurring during a natural, a societal, or a technological catastrophe. This model was developed in collaboration with geographers and psychologists. To collect information on the level of stress, psychologists of the LPPL laboratory of Nantes (France) led virtual reality experiments. These experiments consisted in immersing individuals in a situation of catastrophe and measuring their electrocardiogram. From the physical and biological data collected, we present the methodology to calibrate the behavioral model. First, a theoretical analysis is carried out to determine (i) if the parameters can be uniquely estimated, (ii) the minimal number of discrete measurements required for the estimation. Then, from these analyses, an estimation procedure is performed to calibrate the mathematical model or at least to have an order magnitude of the model parameters. Through this work, we will show from simulations that the proposed system makes it possible to apprehend non observable human processes.

Estimation for Flexible Weibull Extension-Burr XII Distribution under Adaptive Type-II Progressive Censoring Scheme

In this paper, based on an adaptive Type-II progressive censoring scheme, estimation of flexible Weibull extension-Burr XII distribution is discussed. Maximum likelihood estimation and asymptotic confidence intervals of the unknown parameters are obtained. The adaptive Metropolis (AM) method is applied to carry out a Bayesian estimation procedure under symmetric and asymmetric loss functions and calculate the credible intervals. A simulation study is carried out to assess the performance of the estimators. Finally, a real life data set is used for illustration purpose.

Determinants of Tax Revenue in Sierra Leone: Application of the ARDL Framework

The aim of this paper is to investigate the determinants of tax revenue in Sierra Leone, over the period 1990Q1 to 2020Q1, within the context of the ARDL estimation procedure. The result from the ARDL Bound test for cointegration suggests that a long-run relationship exists among the variables. The long run analysis indicates that real GDP (Y), openness (Op) and official development assistance (ODA) are the main determinants of tax revenue (TR) in Sierra Leone, with positive coefficients. This result is in tandem with the short run findings, which establishes a positive relationship between tax revenue and its regressors- real GDP and openness. However, the short run result also suggests that inflation has a negative impact on tax revenue. The findings confirm that any short-run disequilibrium to the long-run can be corrected at the 11 percent speed of adjustment quarterly, albeit at a low speed of adjustment. The diagnostic result shows that approximately 75 percent of the variation in the dependent variable is explained by the regressors based on the value of the R-squared. It also confirms that the model is free of serial correlation and heteroscedasticity, whilst the CUSUM test indicates stability of the model coefficients. The policy implication is for government to pursue policies that will enhance economic growth, through investment in growth enhancing sectors including agriculture, health, education, energy and infrastructure development; and ensure a politically stable environment as a recipe for private sector investment.

An Extended log-Lindley-G Family: Properties and Experiments in Repairable Data

In this article, two new families of distributions are proposed: the generalized log-Lindley-G (GLL-G) and its counterpart, the GLL*-G. These families can be justified by their relation to the log-Lindley model, an important assumption for describing social and economic phenomena. Specific GLL models are introduced and studied. We show that the GLL density is rewritten as a two-member linear combination of the exponentiated G-densities and that, consequently, many of its mathematical properties arise directly, such as moment-based expressions. A maximum likelihood estimation procedure for the GLL parameters is provided and the behavior of the resulting estimates is evaluated by Monte Carlo experiments. An application to repairable data is made. The results argue for the use of the exponential law as the basis for the GLL-G family.

A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations

The ridge regression estimator is a commonly used procedure to deal with multicollinear data. This paper proposes an estimation procedure for high-dimensional multicollinear data that can be alternatively used. This usage gives a continuous estimate, including the ridge estimator as a particular case. We study its asymptotic performance for the growing dimension, i.e., p→∞ when n is fixed. Under some mild regularity conditions, we prove the proposed estimator’s consistency and derive its asymptotic properties. Some Monte Carlo simulation experiments are executed in their performance, and the implementation is considered to analyze a high-dimensional genetic dataset.

Farm-Scale Greenhouse Gas Emissions’ Decision Support Systems

In this paper, 15 farm-scale Green House Gas-based (GHG-based) decision support (DS) tools were evaluated based on a number of criteria (descriptive evaluation), as well as the parameters requested as inputs and the outputs, all of which are considered important for the estimation procedure and the decision support approach. The tools were grouped as emission calculators and tools providing indicators in terms of more than one pillar of sustainability. The results suggest an absence of automatic consultation in decision support in most of the tools. Furthermore, dairy and beef cattle production systems are the most represented in the tools examined. This research confirms a number of important functionalities of modern GHG-based DS tools.

PORTFOLIO INSURANCE UNDER ROUGH VOLATILITY AND VOLTERRA PROCESSES

Affine Volterra processes have gained more and more interest in recent years. In particular, this class of processes generalizes the classical Heston model and the more recent rough Heston model. The aim of this work is hence to revisit and generalize the constant proportion portfolio insurance (CPPI) under affine Volterra processes. Indeed, existing simulation-based methods for CPPI do not apply easily to this class of processes. We instead propose an approach based on the characteristic function of the log-cushion which appears to be more consistent, stable and particularly efficient in the case of saffine Volterra processes compared with the existing simulation techniques. Using such approach, we describe in this paper several properties of CPPI which naturally result from the form of the log-cushion’s characteristic function under affine Volterra processes. This allows to consider more realistic dynamics for the underlying risky asset in the context of CPPI and hence build portfolio strategies that are more consistent with financial data. In particular, we address the case of the rough Heston model, known to be extremely consistent with past data of volatility. By providing a new estimation procedure for its parameters based on the PMCMC algorithm, we manage to study more accurately the true properties of such CPPI strategy and to better handle the risk associated with it.