Appendix: A general regression model for analysis of independent maternal and paternal age effects for 47,+21 and other disorders that may arise from mutant gametes from either parent

1987 ◽  
Vol 77 (4) ◽  
pp. 314-316 ◽  
Author(s):  
Ernest B. Hook
Keyword(s):  
Regression Model ◽  
Age Effects ◽  
Paternal Age ◽  
General Regression

Parental breeding age effects on descendants’ longevity interact over two generations in matrilines and patrilines

2019 ◽  
Author(s):  
Zac Wylde ◽  
Foteini Spagopoulou ◽  
Amy K Hooper ◽  
Alexei A Maklakov ◽  
Russell Bonduriansky
Keyword(s):  
Maternal Age ◽  
First Generation ◽  
Age Effects ◽  
Population Variation ◽  
Paternal Age ◽  
Interactive Effects ◽  
Competitive Environment ◽  
Larval Diet ◽  
Negative Effects ◽  
Two Generations

Individuals within populations vary enormously in mortality risk and longevity, but the causes of this variation remain poorly understood. A potentially important and phylogenetically widespread source of such variation is maternal age at breeding, which typically has negative effects on offspring longevity. Here, we show that paternal age can affect offspring longevity as strongly as maternal age does, and that breeding age effects can interact over two generations in both matrilines and patrilines. We manipulated maternal and paternal ages at breeding over two generations in the neriid fly Telostylinus angusticollis. To determine whether breeding age effects can be modulated by the environment, we also manipulated larval diet and male competitive environment in the first generation. We found separate and interactive effects of parental and grandparental ages at breeding on descendants’ mortality rate and lifespan in both matrilines and patrilines. These breeding age effects were not modulated by grandparental larval diet quality or competitive environment. Our findings suggest that variation in maternal and paternal ages at breeding could contribute substantially to intra-population variation in mortality and longevity.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Parental Age and Schizophrenia

1966 ◽  
Vol 112 (490) ◽  
pp. 899-905 ◽  
Author(s):  
K. L. Granville-Grossman
Keyword(s):  
Maternal Age ◽  
Age Effect ◽  
Age Effects ◽  
Paternal Age ◽  
Considerable Importance ◽  
Parental Age ◽  
Advanced Paternal Age ◽  
Older Parents ◽  
Maternal Age Effect ◽  
Environmental Causes

Reports that schizophrenics have older parents than non-schizophrenics (Barry, 1945; Goodman, 1957; Johanson, 1958; Gregory, 1959) are of considerable importance. If valid, they provide evidence for environmental causes of schizophrenia, and by analogy with other conditions where parental age effects have been noted may give some indication of the nature of these causes. There are, however, inconsistencies in these studies: thus Johanson and Gregory found a significant association between advanced paternal age and schizophrenia, but failed to confirm the maternal age effect noted by Barry and Goodman. These differences indicate the need for further investigation and this paper describes such a study.

Haar Wavelets in Data Analysis

2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Approximation Theory ◽  
Regression Models ◽  
Haar Wavelets ◽  
General Regression

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

scholarly journals Credibility in the Regression case Revisited (A Late Tribute to Charles A. Hachemeister)

1997 ◽  
Vol 27 (1) ◽  
pp. 83-98 ◽  
Author(s):  
H. Bühlmann ◽  
A. Gisler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Line ◽  
Good Choice ◽  
Design Matrix ◽  
Simple Linear Regression ◽  
One Dimensional ◽  
Regression Parameters ◽  
Definition Of ◽  
General Regression

AbstractMany authors have observed that Hachemeisters Regression Model for Credibility – if applied to simple linear regression – leads to unsatisfactory credibility matrices: they typically ‘mix up’ the regression parameters and in particular lead to regression lines that seem ‘out of range’ compared with both individual and collective regression lines. We propose to amend these shortcomings by an appropriate definition of the regression parameters:–intercept–slopeContrary to standard practice the intercept should however not be defined as the value at time zero but as the value of the regression line at the barycenter of time. With these definitions regression parameters which are uncorrected in the collective can be estimated separately by standard one dimensional credibility techniques.A similar convenient reparametrization can also be achieved in the general regression case. The good choice for the regression parameters is such as to turn the design matrix into an array with orthogonal columns.

scholarly journals Impact of Paternal Age at Conception on Human Health

2019 ◽  
Vol 65 (1) ◽  
pp. 146-152 ◽  
Author(s):  
Mathieu Simard ◽  
Catherine Laprise ◽  
Simon L Girard
Keyword(s):  
Maternal Age ◽  
De Novo ◽  
Autism Spectrum ◽  
Age Effect ◽  
Age Effects ◽  
Paternal Age ◽  
De Novo Mutations ◽  
Brain Functions ◽  
Offspring Health ◽  
Paternal Age Effect

Abstract BACKGROUND The effect of maternal age at conception on various aspects of offspring health is well documented and often discussed. We seldom hear about the paternal age effect on offspring health, although the link is now almost as solid as with maternal age. The causes behind this, however, are drastically different between males and females. CONTENT In this review article, we will first examine documented physiological changes linked to paternal age effect. We will start with all morphological aspects of the testis that have been shown to be altered with aging. We will then move on to all the parameters of spermatogenesis that are linked with paternal age at conception. The biggest part of this review will focus on genetic changes associated with paternal age effects. Several studies that have established a strong link between paternal age at conception and the rate of de novo mutations will be reviewed. We will next discuss paternal age effects associated with telomere length and try to better understand the seemingly contradictory results. Finally, severe diseases that affect brain functions and normal development have been associated with older paternal age at conception. In this context, we will discuss the cases of autism spectrum disorder and schizophrenia, as well as several childhood cancers. SUMMARY In many Western civilizations, the age at which parents have their first child has increased substantially in recent decades. It is important to summarize major health issues associated with an increased paternal age at conception to better model public health systems.

DEVELOPMENT: There's Something Curious About Paternal-Age Effects

Science ◽  
2003 ◽  
Vol 301 (5633) ◽  
pp. 606-607 ◽  
Author(s):  
J. F. Crow
Keyword(s):  
Age Effects ◽  
Paternal Age

scholarly journals Paternal age effects on spermFOXK1andKCNA7methylation and transmission into the next generation

2016 ◽  
pp. ddw328 ◽  
Author(s):  
Stefanie Atsem ◽  
Juliane Reichenbach ◽  
Ramya Potabattula ◽  
Marcus Dittrich ◽  
Caroline Nava ◽  
...  
Keyword(s):  
Age Effects ◽  
Paternal Age ◽  
Next Generation
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