In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare th...

throughout the current study, the appropriate function for a description of egg production curve in a commercial broiler dam line was determined through fittings of a number of six mathematical models, namely: incomplete gamma function (wm): modified incomplete gamma function (mwm): compartmental function (cm): modified compartmental function )mcm): polynomial regression function of ali and sch...

While the conditional mean is known to provide the minimum mean square error (MSE) forecast – and hence is optimal under a squared-error loss function – it must often in practice be replaced by a noisy estimate when model parameters are estimated over a small sample. Here two results are obtained, both of which motivate the use of forecasts biased toward zero (shrinkage forecasts) in such setti...

We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithms that utilize the weighted block Newton (WBN) and variable step size WBN (VSSWBN) method, in which the weighting matrix is determined in the form of the inverse of the squared error or as an exponential function with a negative exponent. The proposed WBN and VSSWBN algorithms converge in two iteratio...

This paper presents methods which are aimed at finding approximations to missing data in a dataset by using optimization algorithms to optimize the network parameters after which prediction and classification tasks can be performed. The optimization methods that are considered are genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), random forest (RF) and negativ...

A method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized riskaverting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity....

Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning appr...

This paper studies three related algorithms: the (traditional) Gradient Descent (GD) Algorithm, the Exponentiated Gradient Algorithm with Positive and Negative weights (EG algorithm) and the Exponentiated Gradient Algorithm with Unnormalized Positive and Negative weights (EGU algorithm). These algorithms have been previously analyzed using the “mistake-bound framework” in the computational lear...