Estimation

The present study comprised of a sample of 150 healthy individuals (75 males and 75 females) studying in various colleges of Delhi. All the individuals were measured for height and hand length. The data thus obtained have been subjected to statistical computation for deriving the regression equations. Bilateral asymmetry in hand measurements were statistically insignificant. Regression equations were derived for right and left hand separately by which living stature may be fairly accurately estimated when a fragmentary or mutilated portion of upper extremity is recovered. Using the regression formula derived in this study, stature can be estimated within the error of +4.0 to 4.6cm from hand length.

This article is a contribution to the discussion on the utility of spatial models in the context of Small Area Estimation (SAE) (see Cressie 1991; Pfeffermann 2002; Saei and Chambers 2003, 2005; Singh et al. 2005; Pratesi and Salvati 2008). The attention is on the Fay–Herriot model and its Mean Squared Error (MSE) when a common autocorrelation parameter among small areas is included. Firstly, we discuss the extent to which the spatial effects in data used for SAE motivate the introduction of an autocorrelation parameter in the Fay–Herriot model. Secondly, the performance of MSE estimators is discussed through a simulation study where the joint effect of the area level sampling variance and of the parameter estimation is shown. The importance of the strength of spatial autocorrelation among small areas is confirmed. The results are tenable for different sampling variance patterns. A case study with spatial dependence in the data is presented and estimates at small area level are prov...

Risk is defined as the probability of a specific adverse event occurring within a specific period, while Quantitative risk assessment (QRA) is the development of a quantitative estimate of risk based on engineering evaluation and mathematical techniques by combining estimate of incident consequences and frequencies. In view of the increase in the use of railways as the mode of transportation for hazardous materials throughout the world, the associated risk analysis should be taken into concern. In this study, the failure frequency of the transportation of ammonia from the Petronas fertilizers Kedah (PFK) plant in Gurun (Northern part of Peninsular Malaysia), to the Chemical Company Malaysia (CCM) fertilizer's facilities in Port Klang (South-western part of Peninsular Malaysia) was evaluated by incorporating the human error assessment. The study highlighted the importance of human error contributions in the failure frequency analysis and its impact on the selected failure scenarios. Besides, it also shows that the application of the human error assessment and reduction technique (HEART), which is a useful human reliability analysis tool, should be used in parallel with the fuzzy arithmetic approach to reduce the uncertainties involved in the estimation of human error probabilities, and hence, to reduce the likelihood of incorrect risks estimates being assessed. The results suggested that the commonly applied approach in quantitative risk assessments, which only consider equipment failures in the failure frequency estimations, are clearly an underestimate of the potential causes of failures leading to hazardous material releases, and hence, the calculated risks based do not reflect the actual risks. © 2008 American Institute of Chemical Engineers Process Saf Prog, 2009

Target tracking in clutter is usually evaluated using simulations. The statistics of the overall tracking performance are gathered, including the false track discrimination, the root mean square trajectory tracking errors, or some combined measure of performance, e.g. OSPA. These performance measures treat the target tracking filter as a black box; i.e. the performance of separate target tracking algorithm components is neither considered nor evaluated. In this paper we partition the target tracking algorithm into the estimation and the data association components and consider the performance measures for the data association component. Three performance measures are proposed and evaluated: the fidelity measure, the selection measure and the competence measure which combines capabilities of the fidelity and the selection measures.

This paper discusses the design and implementation of frequency offset estimation algorithms for DOCSIS upstream channels. A cost-effective estimator which approaches the Cramer-Rao bound for high SNRs is derived. The effect of ISI generated by upstream micro-reflections in typical cable networks is considered, and a condition upon the transmitted preamble sequence which guarantees unbiased estimation is presented. It is shown

1. To assess the accuracy of the gestation-adjusted projection method in estimating birth weight in Thai pregnant women. 2. To determine the efficiency of the gestation-adjusted projection method for the detection of low actual birth weight, normal birth weight, and large fetus groups. Diagnostic clinical trial. The present study was conducted on 328 uncomplicated pregnancies that were monitored at the Department of Obstetrics and Gynecology, Charoenkrung Pracharuk Hospital during the period of August I to November 30, 2006. The fetal biometry was measured by ultrasound at 34 weeks' gestation or after Fetal weight was calculated according to Hadlock's formula. The extrapolation technique is based on the gestation-adjusted projection (GAP) method. The accuracy of the method was assessed by analyzing the weight predictions in relation to the actual birth weight (ABW). Main outcome measurements were simple error absolute error absolute percentage error and accuracy within 10% o...

The use of dynamical system techniques, optimization methods and statistical algorithms to estimate the characteristics of brain electrical activity are explored. A system approach for characterizing EEG (electroencephalogram) signals, based on nonlinear estimation of dynamical characteristics and modeling the evolution of dynamical processes over time is applied. The dynamical characteristics can be used to better visualize the “state vector” of epileptic EEG signals and for the purpose of pattern recognition. An optimization method for reconstructing parameter spaces of dynamical systems is applied to systems with one or more hidden variables, and can be used to reconstruct maps or differential equations of the brain dynamics. The methods are illustrated by using numerically generated data and EEG data from epileptic patients.

Data mining is a process to use statistical technique, mathematics, artificial intelligence, and learning machine to extract, identify beneficial information and discovery knowledge from database. In this research, the authors apply this method to estimate the growth of local sheep. Research method consists of several phases, namely: Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evolution and Knowledge Presentation. Data as amount of 4357 samples, processed by using CART (Classification and Regression Tree) and Correlation Analysis method. The Average Daily Gain is target variable is and indicator variable consist of dry matter intake from : Grass; Corn; Cassava Meal; Coconut Meal; CaCO3; Salt; Premix; Urea; Corn Oil; Corncob; Soybean Meal; Fish Meal and Sunflower Oil. The knowledge presentation gotten is Coconut Meal as dominant indicator variable. The optimal regression trees that has 41 terminal nodes with relative error of 0,659, can ...

An approach is presented to the problem of detecting the number of sources impinging on a passive sensor array that is based on J. Rissanen's (1983) minimum description length (MDL) principle. The approach is applicable to any type of sources, including the case of sources which are fully correlated, referred to as the coherent signals case. Two slightly different detection criteria are derived, both requiring the estimation of the locations of the sources. The first is tailored to the detection problem per se, whereas the ...

This paper presents the evaluation of the performance, in terms of uncertainty, of a tool designed to estimate the main parameters of a model of a photovoltaic panel (PVP) under real and/or simulated working conditions. The presented tool permits the characterization of the panel, and it is useful to predict its behavior in whatever working condition; in this way, it is possible to compare the actual and expected performance to prevent any decrease in the output power, so permitting the replacement of the monitored module before it goes out of order or its efficiency falls under a given threshold. The well-known two-diode model is used to estimate the parameters of the electrical equivalent circuit of the PVP and to simulate the I -V and P-V characteristic curves in any given environmental condition of irradiance and/or temperature. The model and the estimation algorithm are implemented with MATLAB functions, whereas data acquisition and result presentation are managed by a LabVIEW graphics user interface. The presented tool has been validated against an experimentally characterized PVP. The environmental parameters of the model such as irradiance and temperature have been set (with their respective uncertainties) during simulations or directly measured during the outdoor tests, whereas the others parameters have been evaluated using a best-fit algorithm on the measured data. The estimation is based on the minimization of a new objective function and on a modified expression of the model resistances, which differ from those mentioned in the available literature. After a review of the state of the art, this paper provides the description of the estimation technique and its validation by means of simulations and experiments. Some results are also provided to illustrate the performance of the proposed test method.

In this work, we address the problem of locally estimating the size of a Peerto-Peer (P2P) network using local information. We present a novel approach for estimating the size of a peer-to-peer (P2P) network, fitting the sum of new neighbors discovered at each iteration of a breadth-first search (BFS) with a logarithmic function, and then using Lambert’s W function to solve a root of a ln(n)+b−n = 0, where n is the network size. With rather little computation, we reach an estimation error of at most 10 percent, only allowing the BFS to iterate to the third level.