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The noterate is a tool for predicting home mortgage rates, it is often skewed and has missing information. The noterate could be affected by incomplete or inaccurate data, thus leading to inaccurate predictions. Financial organizations including mortgage companies or banks need to consider the risk of uncertainty carefully and make a more accurate prediction based on some suitable models. To deal with this situation, in this paper we compared six computational statistical methods, including the ordinary least square model, maximum likelihood estimation, maximum a posterior, bootstrapping, Metropolis-Hastings, and Gibbs sampling method on a mortgage dataset. Based on the k fold cross-validation technique and four metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), the bootstrapping method outperforms other methods. In practice, this method is recommended for predicting noterate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a stochastic model for epidemiology data. The proposed model is obtained as a random perturbation of a suitable parameter in a deterministic SEIR system. This perturbation allows us to obtain a set of coupled stochastic differential equations (SDEs) that still have the conservation law. Afterward, by using Girsanov's Theorem, we calculate the maximum likelihood estimation (MLE) for parameters that represent the symptomatic infection rate, asymptomatic infection rate, and the proportion of symptomatic individuals. These parameters are crucial to obtain information about the dynamic of the disease. We prove the consistency of the MLE for a fixed time observation window, in which the disease is in its growth phase. The proposed stochastic SEIR model improves the uncertainty quantification of an overestimated MCMC scheme based on its deterministic model to count reported-confirmed COVID-19 cases of Mexico City. Using a particular mechanism to manage missing data, we developed MLE for some parameters of the stochastic model that improves the description of variance of the actual data. [ABSTRACT FROM AUTHOR]

The present paper deals with several aspects and procedures of identification in a financial market model with time-dependent volatility function and mean reverting stochastic drift term. For this term, an external source of randomness is permitted. In this context, the corresponding inverse problem of option pricing is considered. Here it is of importance that the classical Black-Scholes formula remains unaffected by the drift term. On the other hand, estimating the quadratic variation of the process, high-frequency asset price data are used directly for calibrating the volatility function. We suggest an estimator that is based on a projection on an orthonormal wavelet basis. Finally, classical maximum likelihood methods are applied to estimate the parameters included in the drift term. [ABSTRACT FROM AUTHOR]

We consider the generalized extended exponential-Weibull distribution, which can be very useful for modelling non-monotonic failure rate function, and provide a detailed study of its structural properties. This model includes at least thirteen sub-models and some of them are very known such as the Weibull, generalized Weibull, exponential-Weibull, and exponentiated generalized linear exponential distributions. We also consider different estimation procedures for estimating the model parameters, namely: maximum likelihood, least-square, weighted least-square, maximum product of spacings, Cramér-von-Mises, and Anderson-Darling methods. We also conduct Monte Carlo simulation experiments to assess the finite sample properties of the proposed estimation methods. The usefulness of the distribution is illustrated by means of two real data sets to prove its versatility in practical applications. [ABSTRACT FROM AUTHOR]