Quasimodularity of the kth residual cranks

We study a family of residual crank generating functions defined on overpartitions, the so-called [Formula: see text]th residual cranks. Specifically, the moment generating functions associated to these cranks exhibit quasimodularity properties which are dependent on the choice of [Formula: see text]. We also show that the second moments of these cranks admit a combinatoric interpretation as weighted overpartition counts. This interpretation gives a refinement to an existing inequality between crank moments of differing modulus [Formula: see text].

Some characterizations for the CIR model with Markov switching

Recently, the Cox–Ingersoll–Ross (CIR) model with Markov switching has been discussed extensively. However, the covariance function and the [Formula: see text]th moment for this model are still open. In this paper, we consider some characterizations for the CIR model with Markov switching. First, the conditional moment generating functions for CIR model with Markov switching are given. Then, explicit expressions for the covariance function and moments of the CIR model with Markov switching are obtained. Finally, several examples have been presented to illustrate our results.

Recurrence Relations for Marginal and Joint Moment Generating Functions of Topp-Leone Generated Exponential Distribution based on Record Values and its Characterization

The exact expressions and some recurrence relations are derived for marginal and joint moment generating functions of kth lower record values from Topp-Leone Generated (TLG) Exponential distribution. This distribution is characterized by using the recurrence relation of the marginal moment generating function of kth lower record values.

A zero-truncated discrete Akash distribution with properties and applications

This study proposes and examines a zero-truncated discrete Akash distribution and obtains its probability and moment-generating functions. Its moments and moments-based statistical constants, including coefficient of variation, skewness, kurtosis, and the index of dispersion, are also presented. The parameter estimation is discussed using both the method of moments and maximum likelihood. Applications of the distribution are explained through three examples of real datasets, which demonstrate that the zero-truncated discrete Akash distribution gives better fit than several zero-truncated discrete distributions.

A New Method for Model-Based Health Economic Evaluation Utilizing and Extending Moment-Generating Functions

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.