scholarly journals Asymptotic Likelihood-Based Prediction Functions

Econometrica ◽  
1990 ◽  
Vol 58 (5) ◽  
pp. 1215 ◽  
Author(s):  
Thomas F. Cooley ◽  
William R. Parke
Keyword(s):  
Prediction Functions ◽  
Asymptotic Likelihood

Service Life Prediction for the Neoprene Used in Seawater

2013 ◽  
Vol 739 ◽  
pp. 85-89
Author(s):  
Qing Zhen Wen ◽  
Chao Yu ◽  
Jin Hua Zhu
Keyword(s):  
Service Life ◽  
Aging Time ◽  
Life Prediction ◽  
Sea Water ◽  
Accelerated Aging ◽  
Service Life Prediction ◽  
Exponential Form ◽  
Prediction Functions

The heat seawater method was designed and the accelerated aging tests of the neoprene were carried in laboratory. The toughness and strength of the neoprene in aging time was investigated. The rule of toughness and strength and aging time was studied. The service life prediction functions of the neoprene were established and service life at 25°C was estimated based on the index of toughness and strength. It is concluded that toughness and strength of the neoprene decreases in exponential form with aging time, and the service life of the neoprene used in sea water at 25°C is 29.5 years.

scholarly journals Time series prediction using a recursive algorithm of a combination of genetic programming and constant optimization

2002 ◽  
Vol 15 (2) ◽  
pp. 265-279 ◽  
Author(s):  
Witthaya Panyaworayan ◽  
Georg Wuetschner
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Recursive Algorithm ◽  
Time Series Prediction ◽  
Prediction Function ◽  
Prediction Functions

In this paper we present a prediction process of Time Series using a combination of Genetic Programming and Constant Optimization. The Genetic Programming will be used to evolve the structure of the prediction function, whereas the Constant Optimization will determine the numerical parameters of the prediction function. The prediction process is applied recursively. In each recursion step, a sub-prediction function is evolved. At the end of the iteration all sub-prediction functions form the final prediction function. The avoiding of a major problem in the prediction called over-fitting is also described in this article.

Predicting soil nitrogen supply from soil properties

2015 ◽  
Vol 95 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Jacynthe Dessureault-Rompré ◽  
Bernie J. Zebarth ◽  
David L. Burton ◽  
Alex Georgallas
Keyword(s):  
Soil Properties ◽  
Growing Season ◽  
Nitrogen Supply ◽  
Regression Equation ◽  
First Order ◽  
Mineralizable N ◽  
Prediction Function ◽  
Prediction Functions ◽  
Satisfactory Prediction ◽  
Soil Nitrogen Supply

Dessureault-Rompré, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N 0) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R 2=0.17–0.18) of SNS when compared with a field-based measure of SNS (PASNS) if values of N 0 were predicted from the regression equation. We also examined a two-pool zero- plus first-order (ZF) prediction function. A regression model was developed including soil organic C and Pool I and explained 66% of the variance in k S , the rate constant of the zero-order pool. In addition, a regression equation was developed which explained 86% of the variance in the size of the first-order pool, N L , from Pool I. The ZF prediction function provided satisfactory prediction of SNS (R 2=0.41–0.49) using both measured and predicted values of k S and N L . This study demonstrated a simple prediction function can be used to estimate SNS over a growing season where the mineralizable N parameters are predicted from simple soil properties using regression equations.

Prediction Functions in Bi-temporal Datastreams

2010 ◽  
pp. 261-268 ◽  
Author(s):  
André Bolles ◽  
Marco Grawunder ◽  
Jonas Jacobi ◽  
Daniela Nicklas ◽  
H. -Jürgen Appelrath
Keyword(s):  
Prediction Functions

scholarly journals Asymptotic likelihood estimation from birth and death on a flow

1996 ◽  
Vol 24 (3) ◽  
pp. 1161-1184 ◽  
Author(s):  
Michael J. Phelan
Keyword(s):  
Likelihood Estimation ◽  
Asymptotic Likelihood

Asymptotic likelihood theory for diffusion processes

1975 ◽  
Vol 12 (2) ◽  
pp. 228-238 ◽  
Author(s):  
B. M. Brown ◽  
J. I. Hewitt
Keyword(s):  
Central Limit Theorem ◽  
Maximum Likelihood ◽  
Limit Theorem ◽  
Continuous Time ◽  
Diffusion Processes ◽  
Maximum Likelihood Estimates ◽  
Stochastic Integrals ◽  
Ratio Test ◽  
Chi Squared ◽  
Asymptotic Likelihood

We investigate the large-sample behaviour of maximum likelihood estimates (MLE's) of the parameters of a diffusion process, which is observed throughout continuous time. The results (limit normal distribution for the MLE and an asymptotic chi-squared likelihood ratio test) correspond exactly to classical asymptotic likelihood results, and follow easily from a central limit theorem for stochastic integrals.

scholarly journals Prediction Functions for Categorical Panel Data

1995 ◽  
Vol 23 (4) ◽  
pp. 1130-1142 ◽  
Author(s):  
Zvi Gilula ◽  
Shelby J. Haberman
Keyword(s):  
Panel Data ◽  
Prediction Functions

scholarly journals Performance of the Framingham and SCORE cardiovascular risk prediction functions in a non-diabetic population of a Spanish health care centre: a validation study

2010 ◽  
Vol 28 (4) ◽  
pp. 242-248 ◽  
Author(s):  
Lourdes Cañón Barroso ◽  
Eloísa Cruces Muro ◽  
Natalio Díaz Herrera ◽  
Gerardo Fernández Ochoa ◽  
Juan Ignacio Calvo Hueros ◽  
...  
Keyword(s):  
Health Care ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Validation Study ◽  
Care Centre ◽  
Health Care Centre ◽  
Cardiovascular Risk Prediction ◽  
Diabetic Population ◽  
Prediction Functions ◽  
Spanish Health

A field experiment for calibrating landslide time-of-failure prediction functions

2014 ◽  
Vol 67 ◽  
pp. 69-77 ◽  
Author(s):  
Francesca Bozzano ◽  
Ivan Cipriani ◽  
Paolo Mazzanti ◽  
Alberto Prestininzi
Keyword(s):  
Field Experiment ◽  
Failure Prediction ◽  
Prediction Functions ◽  
Time Of Failure

An asymptotic likelihood ratio test for a markov chain with bernoullian or contingent input

Psychometrika ◽  
1968 ◽  
Vol 33 (4) ◽  
pp. 405-422 ◽  
Author(s):  
Richard S. Bogartz
Keyword(s):  
Markov Chain ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Ratio Test ◽  
Asymptotic Likelihood
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