Least absolute deviation (LAD) regression is an important tool used in numerous applications throughout science and engineering, mainly due to the intrinsic robust characteristics of LAD. In this paper, we show that the optimization needed to solve the LAD regression problem can be viewed as a sequence of maximum likelihood estimates (MLE) of location. The derived algorithm reduces to an iterat...

In this paper we show how a discriminative objective function such as Maximum Mutual Information (MMI) can be combined with a prior distribution over the HMM parameters to give a discriminative Maximum A Posteriori (MAP) estimate for HMM training. The prior distribution can be based around the Maximum Likelihood (ML) parameter estimates, leading to a technique previously referred to as I-smooth...

Abstract In this paper, we present a new multiperiod portfolio selection model with maximum absolute deviation model. The investor is assumed to seeks an investment strategy to maximize his/her terminal wealth, and minimize the total risk in all periods. Different with original consideration that risk is defined as the variance of terminal wealth, in our paper, the total risk is defined as the ...

Development, production quality control and calibration of optical tissue-mimicking phantoms require a convenient and robust characterization method with known absolute accuracy. We present a solid phantom characterization technique based on time resolved transmittance measurement of light through a relatively small phantom sample. The small size of the sample enables characterization of every ...

PURPOSE To quantify the effect on refractive outcomes after cataract surgery of personalization of Haigis intraocular lens (IOL) constants for a given surgeon-IOL combination. SETTING Institute of Eye Surgery and Institute of Vision Research, Whitfield Clinic, Butlerstown North, Waterford, Ireland. METHODS Personalization of Haigis IOL constants was performed using a series of 248 suitable ...

background: water is considered as the main source of life but water resources are limited and nonrenewable. different factors have caused groundwater to decrease. therefore, modeling and predicting groundwater level is of great importance. methods: monthly groundwater level data of about 20 years (october 1991 to february 2012) from the hamadan-bahar plain, west of iran were used based on peiz...

In multiple-input multiple-output (MIMO) channel (H) communication, when channel status information (CSI) is known to the receiver but not to the transmitter, the precoding technique can achieve a highly reliable communication link, when the receiver informs an optimal precoding matrix index to the transmitter based on current CSI. To select an optimal precoding matrix (F), the maximum capacity...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting. In order provide the forecasted energy consumption, the ANN interpolates between the EEC and its determinants in a training data set. In this study, two ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values....