A system for compatible prediction of total and merchantable volumes allowing for different definitions of tree volume

2010 ◽  
Vol 40 (4) ◽  
pp. 747-760 ◽  
Author(s):  
Luís Nunes ◽  
José Tomé ◽  
Margarida Tomé
Keyword(s):  
Volume Ratio ◽  
Predictive Ability ◽  
Generalized Least Squares ◽  
Seemingly Unrelated Regression ◽  
Maritime Pine ◽  
Regression Equations ◽  
Least Squares Regression ◽  
Data Set ◽  
Forest Inventories ◽  
Taper Equation

A system of equations for compatible prediction of total and merchantable volumes that allows for different definitions of tree volume was developed in this study. The use of the developed system will allow the conversion and subsequent comparison of results from forest inventories using different definitions of tree volume (e.g., including or not the top material of the tree and (or) the stump, inside or outside bark). The compatibility between taper, total volume, and volume ratio equations is ensured by properly integrating the taper equation. The diameter under the bark at any height is modeled with the Demaerschalk taper equation, and the corresponding diameter over the bark is obtained by assuming that bark thickness is also modeled with Demaerschalk’s function. The set of equations that has contemporaneous cross-equation error correlation (known as nonlinear seemingly unrelated regression equations) was fit using nonlinear joint generalized least squares regression. The predictive ability was evaluated using an independent data set. The system is consistent and performs well when applied to maritime pine ( Pinus pinaster Ait.) trees in Portugal, showing better performance than do other total volume equations for maritime pine used in the latest Portuguese national forest inventories.

The relationship between cross-border acquisitions and R&D investments by Indian firms – substituting or complementing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajay Kumar Singal ◽  
Faisal Mohammad Ahsan
Keyword(s):  
Resource Constraints ◽  
Emerging Market ◽  
Main Idea ◽  
Generalized Least Squares ◽  
Emerging Economy ◽  
Least Squares Regression ◽  
Data Set ◽  
Content Type ◽  
Cross Border ◽  
The Relationship

PurposeEmerging economy firms seek strategic assets through cross-border acquisitions (CBAs) to upgrade their capabilities. The paper explores the relation between emerging economy firms' investments in CBAs and subsequent investments in domestic R&D. It investigates the underlying mechanism that links a firm's decision to pursue CBAs and the outcomes from the CBAs. The main idea behind the study is that firms have higher possibility of creating value from cross-border acquisitions when they simultaneously invest in domestic R&D though both investments are constrained by financial and managerial resources.Design/methodology/approachThe hypotheses are tested on a panel data set of 296 Indian firms over a period of 13 years (2003–2015). The authors use a two-stage Heckman procedure for testing their hypotheses. In the first stage, a probit model predicts the probability of a firm being a cross-border acquirer. The second stage model is estimated by a pooled-data GLS (generalized least squares) regression technique.FindingsThe authors find a nonlinear (inverted U-shaped) relationship between firm's investments in CBAs and domestic R&D. This suggests a complementary relation between investments in CBAs and a firm's domestic R&D at lower levels of investments in CBAs. At higher levels of investments in CBAs, CBA investments begin to substitute for firm's domestic R&D investments. For firms with higher international product-market experience and those operating in the hi-tech industry, the relationship between investments in CBAs and domestic R&D is complementary even at higher levels of CBA investments.Originality/valueThe study highlights the role of an emerging market firm's investment in domestic R&D as a link between the decision to invest in CBAs and related outcomes thereof. Emerging market firms face resource constraints while pursuing simultaneous investments in CBAs and R&D, but investment in R&D is essential for realizing the acquisition objectives. The authors also establish the significance of industry context and experiential learning in deciding the allocation of resources between CBAs and internal R&D.

scholarly journals The Consumption Expenditure of Households in Turkey: Demand System Estimation with Pseudo Panel Data

2013 ◽  
Author(s):  
Seda Şengül ◽  
Çiler Sigeze
Keyword(s):  
Household Size ◽  
Seemingly Unrelated Regression ◽  
Alcoholic Beverages ◽  
Demand System ◽  
Household Expenditure ◽  
Regression Equations ◽  
Data Set ◽  
Goods And Services ◽  
Statistical Institute ◽  
Demand System Estimation

In this study, micro data sets obtained by 2005 and 2009 Household Budget Surveys compiled by Turkish Statistical Institute were used to estimate the parameters of household consumption demand and calculate the income-demand elasticities of consumer goods. Total expenditures of the households in this data set delivered into the following 12 different categories of goods and services. The expenditure share of these different categories of goods and services is the dependent variable of this model. In addition, the total household expenditure, the squared total household expenditure, the household size adjusted in accordance with the OECD equivalence scale and the logarithms of squared household size are the independent variables used in the study. The Seemingly Unrelated Regression Equations (SURE) is used to estimate the Quadratic Almost Ideal Demand System (QAIDS) so as to determine the demand parameters of the main commodity groups. The principal result of the study is that the consumption elasticities of the food and nonalcoholic beverages, housing, water, electricity, fuel, clothing and footwear, furniture and house appliances, communications, alcoholic beverages, cigarette and tobacco expenditure are less than 1. Therefore, it can be said that these commodity groups are considered to be mandatory goods. On the other hand, the consumption elasticities of the health, transportation, education services, entertainment and culture, restaurants, hotels, patisseries are more than 1. Thus, these commodity groups are considered to be luxury goods. In this regard, the study concludes that Turkey is considered to be a developing country in terms of the consumption characteristics.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 506
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernández ◽  
Julio César Mello-Román
Keyword(s):  
Kernel Function ◽  
Linear Method ◽  
Predictive Ability ◽  
Feature Space ◽  
Memetic Algorithms ◽  
Least Squares Regression ◽  
Optimal Parameters ◽  
Number Of Components ◽  
Kernel Partial Least Squares ◽  
Dependent Variables

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

scholarly journals A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions

2021 ◽  
Author(s):  
Simone Persiano ◽  
Jose Luis Salinas ◽  
Jery Russell Stedinger ◽  
William H. Farmer ◽  
David Lun ◽  
...  
Keyword(s):  
Least Squares ◽  
Generalized Least Squares ◽  
Least Squares Regression

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

Predictive view of the value relevance of earnings in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hajam Abid Bashir ◽  
Manish Bansal ◽  
Dilip Kumar
Keyword(s):  
Capital Investment ◽  
Stock Return ◽  
Value Relevance ◽  
Stock Exchange ◽  
Predictive Ability ◽  
Cash Flows ◽  
Data Set ◽  
Content Type ◽  
Uniform Sample ◽  
Reported Earnings

Purpose This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings. Design/methodology/approach The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables. Findings Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results. Originality/value This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

Predicting the risk of respiratory distress in newborns with congenital pulmonary malformations

2021 ◽  
pp. 2100949
Author(s):  
Christophe Delacourt ◽  
Nathalie Bertille ◽  
Laurent J. Salomon ◽  
Makan Rashenas ◽  
Alexandra Benachi ◽  
...  
Keyword(s):  
Respiratory Distress ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Volume Ratio ◽  
Predictive Ability ◽  
Population Based ◽  
Term Infants ◽  
Predictive Variable ◽  
Study Results ◽  
Roc Area

ObjectivesMost children with prenatally diagnosed congenital pulmonary malformations (CPM) are asymptomatic at birth. We aimed to develop a parsimonious prognostic model for predicting the risk of neonatal respiratory distress (NRD) in preterm and term infants with CPM, based on the prenatal attributes of the malformation.MethodsMALFPULM is a prospective population-based nationally representative cohort including 436 pregnant women. The main predictive variable was the CPM volume ratio (CVR) measured at diagnosis (CVR first) and the highest CVR measured (CVR max). Separate models were estimated for preterm and term infants and were validated by bootstrapping.ResultsIn total, 67 of the 383 neonates studied (17%) had NRD. For infants born at term (>37 weeks, N=351), the most parsimonious model included CVR max as the only predictive variable (ROC area: 0.70±0.04, negative predictive value: 0.91). The probability of NRD increased linearly with increasing CVR max and remained below 10% for CVR max<0.4. In preterm infants (N=32), both CVR max and gestational age were important predictors of the risk of NRD (ROC area: 0.85±0.07). Models based on CVR first had a similar predictive ability.ConclusionsPredictive models based exclusively on CVR measurements had a high negative predictive value in infants born at term. Our study results could contribute to the individualised general risk assessment to guide decisions about the need for newborns with prenatally diagnosed CPM to be delivered at specialised centers.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

2020 ◽  
Author(s):  
Yanyun Zhao ◽  
Rong Ma ◽  
Fangxiao Liu ◽  
Liwen Zhang ◽  
Xuemei Lv ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Time Dependent ◽  
Net Benefit ◽  
Data Set ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Mutation Profile

Abstract Background: Emerging studies have shown that a variety of gene mutations occur in development and progression of cancer and highly mutation genes could play oncogenic or tumor suppressive roles in cancer. Therefore, our aim is to explore mutation genes which affect the prognosis of bladder.Methods: Mutation profile was obtained and analyzed from TCGA data set. A mutation-based signature was established by multivariable Cox regression analysis. Kaplan-Meier was performed to assess the prognostic power of signature. Time-dependent ROC was conducted to evaluate predictive accuracy of signature for bladder cancer patients.Results: There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%). In this study, we assessed the prognostic predictive ability of 662 highly mutated genes and identified a mutation signature as an independent indicator for predicting the prognosis of bladder. The time-dependent ROC showed that AUC were 0.893, 0.896, 0.916 and 0.965 at 1, 3, 5 and 10 year, respectively. Stratified analysis and Multivariate Cox analysis showed that this mutation signature was reliable and independent biomarker. Furthermore, the nomogram predictive model can be used to effectively predict clinical prognosis of bladder patients. The decision analysis curve showed patients with risk threshold of 0.03-0.92 potentially yielded clinical net benefit. Finally, we identified several signaling pathways that associated with risk score by GSEA and KEGG analysis including PI3K-Akt signaling pathway and so on.Conclusions: In general, this study provide an optimal mutation signature as potential prognosis biomarker for bladder patients.

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