scholarly journals Estimating Optimal Weights for Compound Scores: A Multidimensional IRT Approach

2018 ◽  
Vol 53 (6) ◽  
pp. 914-924
Author(s):  
Hendrika G. van Lier ◽  
Liseth Siemons ◽  
Mart A.F.J. van der Laar ◽  
Cees A.W. Glas
Keyword(s):  
Multidimensional Irt ◽  
Optimal Weights

Q-optimal experimental designs and close to them experimental designs for polynomial regression on the interval

2020 ◽  
Vol 86 (5) ◽  
pp. 65-72
Author(s):  
Yu. D. Grigoriev
Keyword(s):  
Optimal Design ◽  
Polynomial Regression ◽  
Direct Consequence ◽  
Experimental Designs ◽  
Optimal Designs ◽  
Software Systems ◽  
General Character ◽  
Support Points ◽  
Optimal Weights ◽  
Universal Expression

The problem of constructing Q-optimal experimental designs for polynomial regression on the interval [–1, 1] is considered. It is shown that well-known Malyutov – Fedorov designs using D-optimal designs (so-called Legendre spectrum) are other than Q-optimal designs. This statement is a direct consequence of Shabados remark which disproved the Erdős hypothesis that the spectrum (support points) of saturated D-optimal designs for polynomial regression on a segment appeared to be support points of saturated Q-optimal designs. We present a saturated exact Q-optimal design for polynomial regression with s = 3 which proves the Shabados notion and then extend this statement to approximate designs. It is shown that when s = 3, 4 the Malyutov – Fedorov theorem on approximate Q-optimal design is also incorrect, though it still stands for s = 1, 2. The Malyutov – Fedorov designs with Legendre spectrum are considered from the standpoint of their proximity to Q-optimal designs. Case studies revealed that they are close enough for small degrees s of polynomial regression. A universal expression for Q-optimal distribution of the weights pi for support points xi for an arbitrary spectrum is derived. The expression is used to tabulate the distribution of weights for Malyutov – Fedorov designs at s = 3, ..., 6. The general character of the obtained expression is noted for Q-optimal weights with A-optimal weight distribution (Pukelsheim distribution) for the same problem statement. In conclusion a brief recommendation on the numerical construction of Q-optimal designs is given. It is noted that in this case in addition to conventional numerical methods some software systems of symbolic computations using methods of resultants and elimination theory can be successfully applied. The examples of Q-optimal designs considered in the paper are constructed using precisely these methods.

scholarly journals The Optimal Selection for Restricted Linear Models with Average Estimator

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Qichang Xie ◽  
Meng Du
Keyword(s):  
Model Selection ◽  
Linear Models ◽  
Weighted Average ◽  
Selection Procedure ◽  
Information Criterion ◽  
Optimal Weights ◽  
Model Average ◽  
Squared Error ◽  
Generalized Information Criterion ◽  
Risk Investment

The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing ak-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.

OPTIMAL PORTFOLIOS OF CORPORATE BONDS AND HOLD TO MATURITY STRATEGIES

2015 ◽  
Vol 10 (02) ◽  
pp. 1550010
Author(s):  
YAACOV KOPELIOVICH
Keyword(s):  
Corporate Bond ◽  
Insurance Companies ◽  
Asset Liability Management ◽  
Optimal Portfolios ◽  
Optimal Weights ◽  
Liability Management ◽  
Simulation Based ◽  
Copula Approach ◽  
Bond Portfolios ◽  
Bond Indices

In this paper, we initiate a research on optimal bond portfolios, that are held to their maturity. We solve the problem analytically for log utility investor in the case of one risky corporate asset. We compare the behavior of these portfolios to equally weighted and portfolios with randomly selected weights. We apply simulation based on Vasicek’s copula approach to derive optimal weights for a corresponding problem involving more than one corporate bond. Further we discover that these portfolios outperform naive investment in constant maturity (CCM) bond indices with a similar maturity horizon. We explain possible application of our findings to boost asset liability management (ALM) strategies for pensions and insurance companies.

In Search of Optimal Weights for U.S. Men and Women

1995 ◽  
pp. 1-20
Author(s):  
Theodore B. VanItallie ◽  
Edward A. Lew
Keyword(s):  
Men And Women ◽  
Optimal Weights

Weighting of Amplitude and Formant Rise Time Cues by School-Aged Children: A Mismatch Negativity Study

2018 ◽  
Vol 61 (5) ◽  
pp. 1322-1333
Author(s):  
Varghese Peter ◽  
Marina Kalashnikova ◽  
Denis Burnham
Keyword(s):  
Rise Time ◽  
Mismatch Negativity ◽  
Standard Stimulus ◽  
Oddball Paradigm ◽  
Acoustic Cues ◽  
Older Children ◽  
Deviant Stimulus ◽  
Cue Weighting ◽  
School Aged Children ◽  
Optimal Weights

Purpose An important skill in the development of speech perception is to apply optimal weights to acoustic cues so that phonemic information is recovered from speech with minimum effort. Here, we investigated the development of acoustic cue weighting of amplitude rise time (ART) and formant rise time (FRT) cues in children as measured by mismatch negativity (MMN). Method Twelve adults and 36 children aged 6–12 years listened to a /ba/–/wa/ contrast in an oddball paradigm in which the standard stimulus had the ART and FRT cues of /ba/. In different blocks, the deviant stimulus had either the ART or FRT cues of /wa/. Results The results revealed that children younger than 10 years were sensitive to both ART and FRT cues whereas 10- to 12-year-old children and adults were sensitive only to FRT cues. Moreover, children younger than 10 years generated a positive mismatch response, whereas older children and adults generated MMN. Conclusion These results suggest that preattentive adultlike weighting of ART and FRT cues is attained only by 10 years of age and accompanies the change from mismatch response to the more mature MMN response. Supplemental Material https://doi.org/10.23641/asha.6207608

Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

2021 ◽  
Author(s):  
Asma Elyounsi ◽  
Hatem Tlijani ◽  
Mohamed Salim Bouhlel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Problem Size ◽  
Functional Link ◽  
Optimal Weights ◽  
Feed Forward Network ◽  
Higher Order Neural Networks ◽  
Artificial Neural

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.

Copula Approach to Multivariate Energy Efficiency Analysis

2021 ◽  
Author(s):  
Mervenur Sözen ◽  
Mehmet Ali Cengiz
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Data Envelopment ◽  
Network Dea ◽  
Dependency Structure ◽  
Optimal Weights ◽  
Joint Efficiency ◽  
Copula Approach

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

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