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An analytical derivation of the conditional moment-generating function (MGF) for a regime-switching nonlinear drift constant elasticity of variance process is established. The proposed model incorporates both regime-switching mechanisms and nonlinear drift components to better capture market phenomena such as volatility smiles and leverage effects. Regime-switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. Closed-form formulas for the MGF under various conditions, which are then applied for option pricing, are also derived. The efficacy and accuracy of the results are validated through a discrete Markov chain simulation. The results obtained from the proposed formulas completely match with those from MC simulations, while requiring significantly less computational time. [ABSTRACT FROM AUTHOR]

This study proposes a new five-parameter distribution called the gamma-exponentiated Weibull–Poisson (GEWP) distribution. As an extension of the exponentiated Weibull–Poisson family, the GEWP distribution offers a more flexible tool for analyzing a wider variety of data due to its theoretically and practically advantageous properties. It encompasses established distributions like the exponential, Weibull, and exponentiated Weibull. The development of the GEWP distribution proposed in this paper is obtained by combining the gamma–exponentiated Weibull (GEW) and the exponentiated Weibull–Poisson (EWP) distributions. Therefore, it serves as an extension of both the GEW and EWP distributions. This makes the GEWP a viable alternative for describing the variability of occurrences, enabling analysis in situations where GEW and EWP may be limited. This paper analyzes the probability distribution functions and provides the survival and hazard rate functions, the sub-models, the moments, the quantiles, and the maximum likelihood estimation of the GEWP distribution. Then, the numerical experiments for the parameter estimation of GEWP distribution for some finite sample sizes are presented. Finally, the comparative study of GEWP distribution and its sub-models is investigated via the goodness of fit test with real datasets to illustrate its potentiality. [ABSTRACT FROM AUTHOR]

This paper describes various selected properties and features of negative binomial (NB) random variables, with special reference to NB2 (i.e., p = 2), and some generalizations to NBp (i.e., p ≥ 2), specifications. It presents new results (e.g., the NBp moment-generating function) with regard to the relationship between a sample mean and its accompanying variance, as well as spatial statistical/econometric numerical and empirical examples, whose parameter estimators are maximum likelihood or method of moment ones. Finally, it highlights the Moran eigenvector spatial filtering methodology within the context of generalized linear modeling, demonstrating it in terms of spatial negative binomial regression. Its overall conclusion is a bolstering of important findings the literature already reports with a newly recognized empirical example of an NB3 phenomenon.

This paper describes various selected properties and features of negative binomial (NB) random variables, with special reference to NB2 (i.e., p = 2), and some generalizations to NBp (i.e., p ≥ 2), specifications. It presents new results (e.g., the NBp moment-generating function) with regard to the relationship between a sample mean and its accompanying variance, as well as spatial statistical/econometric numerical and empirical examples, whose parameter estimators are maximum likelihood or method of moment ones. Finally, it highlights the Moran eigenvector spatial filtering methodology within the context of generalized linear modeling, demonstrating it in terms of spatial negative binomial regression. Its overall conclusion is a bolstering of important findings the literature already reports with a newly recognized empirical example of an NB3 phenomenon. [ABSTRACT FROM AUTHOR]

Abstract Multiple-input–multiple-output (MIMO) systems have effectively addressed today’s high demand for 5G communications and beyond. Further, MIMO-assisted space–time block codes (STBCs) have been shown to enhance the system’s performance and provide a complete variety of coherent flat-fading channels. Additionally, the closed-form expression of the probability density function of the summation of correlated Weibull random variables remains unknown. In this work, we investigate the performance analysis of MIMO-STBC-enabled systems subject to the Weibull fading channel. New tight approximate expressions for numerous system performance metrics, e.g., outage probability, average capacity under various rate adaption methods, and average symbol/bit error rate, have been obtained. The Monte Carlo simulation method has corroborated all the presented results.

Multiple-input–multiple-output (MIMO) systems have effectively addressed today's high demand for 5G communications and beyond. Further, MIMO-assisted space–time block codes (STBCs) have been shown to enhance the system's performance and provide a complete variety of coherent flat-fading channels. Additionally, the closed-form expression of the probability density function of the summation of correlated Weibull random variables remains unknown. In this work, we investigate the performance analysis of MIMO-STBC-enabled systems subject to the Weibull fading channel. New tight approximate expressions for numerous system performance metrics, e.g., outage probability, average capacity under various rate adaption methods, and average symbol/bit error rate, have been obtained. The Monte Carlo simulation method has corroborated all the presented results. [ABSTRACT FROM AUTHOR]

In recent years, various forms of progressive hybrid censoring schemes (PHCS) have gained significant traction in survival and reliability analysis studies due to their versatility. However, these PHCS variants are often characterized by complexity stemming from the multitude of parameters involved in their specification. Consequently, the primary objective of this paper is to propose a unified approach termed combined type II progressive hybrid censoring scheme ( ComT 2 PHCS) capable of encompassing several existing PHCS variations. Our analysis focuses specifically on the exponential distribution (ExDist). Bayesian inference techniques are employed to estimate the parameters of the ExDist under the ComT 2 PHCS. Additionally, we conduct fundamental distributional analyses and likelihood inference procedures. We derive the conditional moment-generating function (CondMGF) of maximum likelihood estimator (MLE) for parameters of the ExDist under ComT 2 PHCS. Further, we use CondMGF for the distribution of MLE for parameters of ExDist under ComT 2 PHCS. Finally, we provide an illustrative example to elucidate the inference methods derived in this paper. [ABSTRACT FROM AUTHOR]

An analytical derivation of the conditional moment-generating function (MGF) for a regime-switching nonlinear drift constant elasticity of variance process is established. The proposed model incorporates both regime-switching mechanisms and nonlinear drift components to better capture market phenomena such as volatility smiles and leverage effects. Regime-switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. Closed-form formulas for the MGF under various conditions, which are then applied for option pricing, are also derived. The efficacy and accuracy of the results are validated through a discrete Markov chain simulation. The results obtained from the proposed formulas completely match with those from MC simulations, while requiring significantly less computational time.

This study proposes a new five-parameter distribution called the gamma-exponentiated Weibull–Poisson (GEWP) distribution. As an extension of the exponentiated Weibull–Poisson family, the GEWP distribution offers a more flexible tool for analyzing a wider variety of data due to its theoretically and practically advantageous properties. It encompasses established distributions like the exponential, Weibull, and exponentiated Weibull. The development of the GEWP distribution proposed in this paper is obtained by combining the gamma–exponentiated Weibull (GEW) and the exponentiated Weibull–Poisson (EWP) distributions. Therefore, it serves as an extension of both the GEW and EWP distributions. This makes the GEWP a viable alternative for describing the variability of occurrences, enabling analysis in situations where GEW and EWP may be limited. This paper analyzes the probability distribution functions and provides the survival and hazard rate functions, the sub-models, the moments, the quantiles, and the maximum likelihood estimation of the GEWP distribution. Then, the numerical experiments for the parameter estimation of GEWP distribution for some finite sample sizes are presented. Finally, the comparative study of GEWP distribution and its sub-models is investigated via the goodness of fit test with real datasets to illustrate its potentiality.

In this work, a novel generalized family of distributions called the odd beta prime is introduced. The linear representations of the proposed family are obtained. The expressions for the moments, the moment-generating function, and entropy are derived. A three-parameter special sub-model of the proposed family called the odd beta prime exponential distribution is proposed. Finally, two real data sets are used to illustrate the usefulness and flexibility of the proposed distribution. [ABSTRACT FROM AUTHOR]

In recent years, various forms of progressive hybrid censoring schemes (PHCS) have gained significant traction in survival and reliability analysis studies due to their versatility. However, these PHCS variants are often characterized by complexity stemming from the multitude of parameters involved in their specification. Consequently, the primary objective of this paper is to propose a unified approach termed combined type II progressive hybrid censoring scheme (ComT2PHCS) capable of encompassing several existing PHCS variations. Our analysis focuses specifically on the exponential distribution (ExDist). Bayesian inference techniques are employed to estimate the parameters of the ExDist under the ComT2PHCS. Additionally, we conduct fundamental distributional analyses and likelihood inference procedures. We derive the conditional moment-generating function (CondMGF) of maximum likelihood estimator (MLE) for parameters of the ExDist under ComT2PHCS. Further, we use CondMGF for the distribution of MLE for parameters of ExDist under ComT2PHCS. Finally, we provide an illustrative example to elucidate the inference methods derived in this paper.

It is well known that the traditional Jensen inequality is proved by lower bounding the given convex function, f (x) , by the tangential affine function that passes through the point (E { X } , f (E { X })) , where E { X } is the expectation of the random variable X. While this tangential affine function yields the tightest lower bound among all lower bounds induced by affine functions that are tangential to f, it turns out that when the function f is just part of a more complicated expression whose expectation is to be bounded, the tightest lower bound might belong to a tangential affine function that passes through a point different than (E { X } , f (E { X })) . In this paper, we take advantage of this observation by optimizing the point of tangency with regard to the specific given expression in a variety of cases and thereby derive several families of inequalities, henceforth referred to as "Jensen-like" inequalities, which are new to the best knowledge of the author. The degree of tightness and the potential usefulness of these inequalities is demonstrated in several application examples related to information theory. [ABSTRACT FROM AUTHOR]

In this paper, we present new inequalities and bounds involving the incomplete gamma function and related functions. Many of these new inequalities and bounds are based upon results, some well known and some not as well known, from probability theory and reliability theory. When these results are combined with other mathematical techniques, some very good upper and lower bounds are obtained. In particular, improvements of previously discussed bounds are presented. [ABSTRACT FROM AUTHOR]

Although smart grids are characterized by self-healing, economy, high efficiency, and security, many hidden dangers exist in the development of smart grids due to a gradually expanding power grid and the continuous access of new energy to the power grid. Therefore, the development of smart grids, especially their reliability, security, and vulnerability, warrants further investigation. In this study, the vulnerability of smart grids is identified, and the vulnerability elements of smart grids are selected. Based on relevant theories, such as credibility and the combination of the credibility-based moment-generating function and the distortion function, a calculation model and framework of the vulnerability index of a smart grid are constructed. An empirical analysis is also conducted. This study provides a scientific basis for analyzing the vulnerability of smart grids and suggesting reasonable preventive measures and auxiliary decision-making information for relevant planning, design, and operation personnel, which contributes to the sustainable and healthy development of smart grids. [ABSTRACT FROM AUTHOR]

In this article, a bimatrix gamma distributions is introduced. Various mathematical properties of the proposed distribution like marginal distributions, expected values, entropies, and moment generating function are derived. Also, distributions of sum, quotient, and product are investigated. Parameter estimation by the method of maximum likelihood is considered. A simulation study is performed to check the finite sample performance of the maximum likelihood estimators.

It is well known that the traditional Jensen inequality is proved by lower bounding the given convex function, f(x), by the tangential affine function that passes through the point (E{X},f(E{X})), where E{X} is the expectation of the random variable X. While this tangential affine function yields the tightest lower bound among all lower bounds induced by affine functions that are tangential to f, it turns out that when the function f is just part of a more complicated expression whose expectation is to be bounded, the tightest lower bound might belong to a tangential affine function that passes through a point different than (E{X},f(E{X})). In this paper, we take advantage of this observation by optimizing the point of tangency with regard to the specific given expression in a variety of cases and thereby derive several families of inequalities, henceforth referred to as “Jensen-like” inequalities, which are new to the best knowledge of the author. The degree of tightness and the potential usefulness of these inequalities is demonstrated in several application examples related to information theory.

In this work, a novel generalized family of distributions called the odd beta prime is introduced. The linear representations of the proposed family are obtained. The expressions for the moments, the moment-generating function, and entropy are derived. A three-parameter special sub-model of the proposed family called the odd beta prime exponential distribution is proposed. Finally, two real data sets are used to illustrate the usefulness and flexibility of the proposed distribution.

The main purpose of this article is to derive several convolutions for generalized Bernoulli and Euler–Genocchi polynomials of order (r , m) , B n (r , m) (x) and A n (r , m) (x) , respectively. These polynomials have been introduced recently and contain the generalized Bernoulli, Euler and Genocchi polynomials as special members. Some of our results extend the results of M. Merca and others concerning Bernoulli numbers and polynomials. Probabilistic interpretations of the presented results are also given. [ABSTRACT FROM AUTHOR]

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, a union bound on the bit error rate (BER) for multiple input multiple output generalized frequency division multiplexing (MIMO-GFDM) systems is derived based on exact pairwise error probabilities. The moment-generating function is used to calculate the exact pairwise error probability under the assumption that a maximum likelihood detector is used at the receiver. A realistic multipath MIMO channel environment is investigated in which the spatial correlation between antennas and the channel estimation errors are included. The Kronecker model and an additive model are used to describe the spatial correlation and channel estimation errors, respectively. The impacts of the spatial correlation and the channel estimation errors on the derived bound are investigated. The performances of MIMO-GFDM systems using different modulation techniques are also examined. Numerical calculations of the union bound and computer-based Monte-Carlo simulations of BER are carried out to verify the derived bound. Numerical results show that the derived union bound is a tight upper bound on the BER for MIMO-GFDM systems in a reasonable E s / N 0 region. [ABSTRACT FROM AUTHOR]

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.

In this paper, a very accurate approximation method for the statistics of the sum of Málaga-M random variates with pointing error (MRVs) is proposed. In particular, the probability density function of MRV is approximated by a Fox's H-function through the moment-based approach. Then, the respective moment-generating function of the sum of N MRVs is provided, based on which the average symbol error rate is evaluated for an N-branch maximal-ratio combining (MRC) receiver. The retrieved results show that the proposed approximate results match accurately with the exact simulated ones. Additionally, the results show that the achievable diversity order increases as a function of the number of MRC diversity branches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel differential faster-than-Nyquist (DFTN) signaling concept that allows us to dispense with any channel estimation at the receiver, while benefiting from a rate boost specific to faster-than-Nyquist (FTN) signaling. More specifically, at the transmitter, differentially modulated phaseshift keying (DPSK) symbols are transmitted with a symbol interval that is smaller than that defined by the Nyquist criterion. The receiver first equalizes the DPSK symbols, which suffer from the effects of FTN-specific inter-symbol interference (ISI), with the aid of frequency-domain equalization (FDE). Then, the equalized symbols are differentially demodulated in a noncoherent manner. This noncoherent detection is enabled, by relying on the fact that FTN-specific ISI is deterministic when the FTN's symbol packing ratio and the roll-off factor of a shaping filter are known in advance of transmissions. Moreover, we derive an analytical bound on the achievable bit error ratio for the proposed DFTN signaling, based on moment-generating functions. This is achieved by introducing an equivalent signal-to-interference-and-noise ratio for the DPSK symbols after FDE. Our simulation results demonstrate that our DFTN receiver is capable of noncoherently demodulating the DFTN symbols when the symbol packing ratio is not substantially low.

Wireless channels exhibit time, frequency, and spatial correlation. Models in literature that study four-branch diversity receivers make assumptions such as independence, constant correlation, exponential correlation, or some other kind between received signals at each antenna. However, these models are not accurate in many scenarios. Addressing this issue, we provide novel results for the moments, moment-generating function, probability density function, and cumulative distribution function of the quadrivariate Rayleigh distribution with an arbitrary correlation model. To the best of our knowledge, our model is the most comprehensive and the only one that can incorporate the 3GPP suggested spatial correlation structure. We use our new results to derive an analytical expression for the moments of the output signal-to-noise ratio of the four-branch equal gain combining receiver and the four-branch maximal ratio combining receiver. We provide original insight about their output signal-to-noise ratio distributions through their higher order moments in different scenarios. Our expressions are valid for all moments. [ABSTRACT FROM AUTHOR]

Cooperative communication using multiple relays improves quality of service (QoS) parameters of a communication system. To verify this improvement, accurate analysis must be done considering the various fading channels and probability density function (PDF) or cumulative distribution function (CDF) of signal-to-noise ratio (SNR) in relays and destination. In this paper, using the moment-generating function (MGF) method and considering Nakagami-m fading channel, the outage probability and effective capacity are analyzed. Obtaining the closed-form expression of outage probability and effective capacity in Nakagami-m fading channel includes complex mathematical calculations; thus, the Rayleigh fading channel is assumed to be a special case of Nakagami-m distribution to achieve simpler mathematical equations. Simulation results verify the mathematical analysis in terms of effective capacity and outage probability. [ABSTRACT FROM AUTHOR]

In the theory of wireless communications, average performance measures (APMs) are widely utilized to quantify the performance gains/impairments in various fading environments under various scenarios, and to comprehend how the factors arising from design/implementation affect system performance. To the best of our knowledge, it has not been yet discovered in the literature how these APMs relate to each other. In this article, to supplement the APM analyses available in the literature we propose that one APM can be calculated using the other APMs instead of using the end-to-end SNR distribution. Particularly, using the Lamperti’s transformation (LT), we propose a tractable approach, which we call LT-based APM analysis, to identify a relationship between any two given APMs such that it is irrespective of SNR distribution. Thereby, we introduce some novel relationships among average channel capacity (ACC), average bit error rate (ABER) and outage probability/capacity (OP/OC) performances, and accordingly present how to obtain ACC from ABER performance and how to obtain OP/OC from ACC performance in fading environments. We demonstrate that the ACC of any communications system can be evaluated empirically without using end-to-end SNR distribution. We consider some numerical examples and simulations to validate our newly derived relationships. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in the pricing of derivatives and calculation of risk measures. Parameters are estimated on three bivariate series, using a two-stage methodology involving method of moments and least squares. Moreover, a scaling factor is added for extra degrees of freedom to match data features. As an application, we consider investment strategies for a portfolio with two risky assets and a risk-free cash account. We calculate value-at-risk (VaR) values at a 95% risk level using both simulation-based and distribution-based methods. A comparison of these VaR values supports the effectiveness of our approximations and the potential for higher dimensions.

Background. Health economic evaluations frequently include projections for lifetime costs and health effects using modeling frameworks such as Markov modeling or discrete event simulation (DES). Markov models typically cannot represent events whose risk is determined by the length of time spent in state (sojourn time) without the use of tunnel states. DES is very flexible but introduces Monte Carlo variation, which can significantly limit the complexity of model analyses. Methods. We present a new methodological framework for health economic modeling that is based on, and extends, the concept of moment-generating functions (MGFs) for time-to-event random variables. When future costs and health effects are discounted, MGFs can be used to very efficiently calculate the total discounted life-years spent in a series of health states. Competing risks are incorporated into the method. This method can also be used to calculate discounted costs and health effects when these payoffs are constant per unit time, one-off, or exponential with regard to time. MGFs are extended to additionally support costs and health effects which are polynomial with regard to time (as in a commonly used model of population norms for EQ-5D utility). Worked Example. A worked example is used to demonstrate the application of the new method in practice and to compare it with Markov modeling and DES. Results are compared in terms of convergence and accuracy, and computation times are compared. R code and an Excel workbook are provided. Conclusions. The MGF method can be applied to health economic evaluations in the place of Markov modeling or DES and has certain advantages over both. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of efficient computation of cross-moments of a vector random variable represented by a stochastic context-free grammar. Two types of cross-moments are discussed. The sample space for the first one is the set of all derivations of the context-free grammar, and the sample space for the second one is the set of all derivations which generate a string belonging to the language of the grammar. In the past, this problem was widely studied, but mainly for the cross-moments of scalar variables and up to the second order. This paper presents new algorithms for computing the cross-moments of an arbitrary order, while the previously developed ones are derived as special cases. [ABSTRACT FROM AUTHOR]

In generalized measure theory, σ-⊕-measure is a generalization of the classical measure defined on a pseudo-addition. In this paper, the class of pseudo-exponential distributions based on a class of σ-⊕-measure is introduced. Some examples of this class are investigated. Then by two real data sets obtained from the last three decades of oil, and the last two decades of the daily natural gas spot prices, we show that the pseudo-exponential distribution is better fitted than exponential distribution using the AIC and BIC information criteria. [ABSTRACT FROM AUTHOR]

In this article, the new family of multivariate skew slash distribution is defined. According to the definition, a stochastic representation of the multivariate skew slash distribution is derived. The first four moments and measures of skewness and kurtosis of a random vector with the multivariate skew slash distribution are obtained. The distribution of quadratic forms for the multivariate skew slash distribution and the non central skew slash χ2 distribution are studied. Maximum likelihood inference and real data illustration are discussed. In the end, the potential extension of multivariate skew slash distribution is discussed. [ABSTRACT FROM AUTHOR]

This work is an extension of our earlier article, where a well-known integral representation of the logarithmic function was explored and was accompanied with demonstrations of its usefulness in obtaining compact, easily-calculable, exact formulas for quantities that involve expectations of the logarithm of a positive random variable. Here, in the same spirit, we derive an exact integral representation (in one or two dimensions) of the moment of a nonnegative random variable, or the sum of such independent random variables, where the moment order is a general positive non-integer real (also known as fractional moments). The proposed formula is applied to a variety of examples with an information-theoretic motivation, and it is shown how it facilitates their numerical evaluations. In particular, when applied to the calculation of a moment of the sum of a large number, n, of nonnegative random variables, it is clear that integration over one or two dimensions, as suggested by our proposed integral representation, is significantly easier than the alternative of integrating over n dimensions, as needed in the direct calculation of the desired moment.

It is a well-known theorem in linear models that the idempotency of a matrix is a sufficient and necessary condition for a quadratic form in normal variates to have a chisquare distribution, but its proofs in the early literature were incorrect or incomplete. Driscoll (1999) provided an improved proof, and this article presents a simple proof. More importantly, we establish and prove a generalized theorem.