scholarly journals The Development of an Ordinary Least Squares Parametric Model to Estimate the Cost Per Flying Hour of ‘Unknown’ Aircraft Types and a Comparative Application †

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 104 ◽  
Author(s):  
Ilias Lappas ◽  
Michail Bozoudis
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Ordinary Least Squares ◽  
Parametric Model ◽  
Development Programs ◽  
Cost Estimating ◽  
Life Cycle Stages ◽  
Wide Range ◽  
High Level ◽  
The Cost

The development of a parametric model for the variable portion of the Cost Per Flying Hour (CPFH) of an ‘unknown’ aircraft platform and its application to diverse types of fixed and rotary wing aircraft development programs (F-35A, Su-57, Dassault Rafale, T-X candidates, AW189, Airbus RACER among others) is presented. The novelty of this paper lies in the utilization of a diverse sample of aircraft types, aiming to obtain a ‘universal’ Cost Estimating Relationship (CER) applicable to a wide range of platforms. Moreover, the model does not produce absolute cost figures but rather analogy ratios versus the F-16’s CPFH, broadening the model’s applicability. The model will enable an analyst to carry out timely and reliable Operational and Support (O&S) cost estimates for a wide range of ‘unknown’ aircraft platforms at their early stages of conceptual design, despite the lack of actual data from the utilization and support life cycle stages. The statistical analysis is based on Ordinary Least Squares (OLS) regression, conducted with R software (v5.3.1, released on 2 July 2018). The model’s output is validated against officially published CPFH data of several existing ‘mature’ aircraft platforms, including one of the most prolific fighter jet types all over the world, the F-16C/D, which is also used as a reference to compare CPFH estimates of various next generation aircraft platforms. Actual CPFH data of the Hellenic Air Force (HAF) have been used to develop the parametric model, the application of which is expected to significantly inform high level decision making regarding aircraft procurement, budgeting and future force structure planning, including decisions related to large scale aircraft modifications and upgrades.

scholarly journals Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

2019 ◽  
Vol 79 (5) ◽  
pp. 883-910 ◽  
Author(s):  
Spyros Konstantopoulos ◽  
Wei Li ◽  
Shazia Miller ◽  
Arie van der Ploeg
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Large Scale ◽  
Social Science Research ◽  
Empirical Work ◽  
Science Research ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Intervention Effects ◽  
Education Data

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).

scholarly journals Analyst Following, Environmental Disclosure and Cost of Equity: Research Based on Industry Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 300 ◽  
Author(s):  
Sheng Yao ◽  
Haotian Liang
Keyword(s):  
Least Squares ◽  
Information Disclosure ◽  
Ordinary Least Squares ◽  
Environmental Information ◽  
Mediating Effect ◽  
Cost Of Equity ◽  
Environmental Disclosure ◽  
Analyst Following ◽  
Environmental Information Disclosure ◽  
The Cost

Prior studies argue that an analyst is an important mediator between a firm and investors, and has a significant influence on the cost of equity. However, how analyst following influences the cost of equity has not been studied in depth. In the Chinese setting, where environmental information has attracted much attention, we explore the interaction among analyst following, environmental information disclosure, and cost of equity. With two linear regression methods of ordinary least squares (OLS) and two-Stage least squares (2SLS), we establish regressions to verify the relationships among them by using empirical data from 2004 to 2011 in China. The results show that analyst following can improve environmental information disclosure and lower the cost of equity. This interaction is more significant in the heavy-pollution industry and after new environmental policy is issued. We also find that environmental disclosure has a mediating effect, which determines how analyst following influences the cost of equity. The results expand the research on environmental information’s motivations and economic consequences.

scholarly journals A Generic Cost Estimating Approach for a Composite Manufacturing Process Assessment

2021 ◽  
Author(s):  
Justyna Rybicka ◽  
Teresa Purse ◽  
Brett Parlour
Keyword(s):  
Cost Estimation ◽  
Model Development ◽  
Manufacturing Processes ◽  
Cost Estimating ◽  
Estimation Model ◽  
Composite Manufacturing ◽  
Composites Manufacturing ◽  
Cost Estimation Model ◽  
High Level ◽  
The Cost

Cost estimation helps build confidence in the feasibility of the development of novel manufacturing processes. This paper focuses on the exploration of the cost estimation for novel manufacturing processes for decision support. One of the aspects of estimation is building credibility around the analysis, especially, in the early stages of planning. Cost estimating guidelines provide a good overview of the cost estimation steps but there is a requirement for guidelines for cost estimation model development. Through building on an understanding of the cost estimation principles, as well as cost estimation modelling, a high-level generic approach for process cost estimation is proposed. Further, a demonstration of a cost estimation modelling approach used for composites manufacturing in the automotive sector is provided, outlining the steps in cost estimation model development.

scholarly journals Does forensic audit influence fraud control? Evidence from Nigerian deposit money banks

2020 ◽  
Vol 15 (2) ◽  
pp. 214-229
Author(s):  
Kehinde Adesina ◽  
Olayinka Erin ◽  
Opeyemi Ajetunmobi ◽  
Simon Ilogho ◽  
Osariemen Asiriuwa
Keyword(s):  
Least Squares ◽  
Internal Control ◽  
Banking Sector ◽  
Survey Design ◽  
Ordinary Least Squares ◽  
Training And Development ◽  
Primary Data ◽  
Regulatory Agencies ◽  
Development Programs ◽  
Structured Questionnaire

This study examines the importance of the application of forensic audit in controlling financial frauds that ravage or threaten the soundness and business continuity of Deposit Money Banks (DMBs) in Nigeria. The study used survey design methods, and the primary data were obtained through the administration of structured questionnaire covering seventeen (17) banks out of twenty-two (22) Deposit Money Banks (DMBs) operating in the country, which is 77.3%. In this study, the Ordinary Least Squares (OLS) method was used to analyze and test hypotheses, and the findings showed that the involvement of qualified and experienced forensic auditors would not only contribute to the amelioration of financial frauds in DMBs, but would also lead to much-needed sanity in the banking sector of Nigeria. The study recommends that regulatory agencies, within the limits prescribed by law, mandate all the banks to create a special forensic department, managed by a professional forensic auditor, which will develop and constantly implement effective and efficient internal control, timely prosecution of fraudsters by considering them to be criminals and as a deterrent to others, and work out adequate training and development programs for their staff, especially in fraud control, in order to reduce the number of fraud cases in Nigerian banks.

scholarly journals Smartphone and Tablet Application (App) Life Cycle Characterization via Apple App Store Rank

2020 ◽  
Vol 4 (1) ◽  
pp. 44-67
Author(s):  
Han Jia ◽  
Chun Guo ◽  
Xiaozhong Liu
Keyword(s):  
Life Cycle ◽  
Least Squares ◽  
Product Life Cycle ◽  
Large Scale ◽  
Ordinary Least Squares ◽  
Heterogeneous Data ◽  
Life Cycles ◽  
Volume Data ◽  
App Store ◽  
Management Domain

AbstractWith the rapid growth of the smartphone and tablet market, mobile application (App) industry that provides a variety of functional devices is also growing at a striking speed. Product life cycle (PLC) theory, which has a long history, has been applied to a great number of industries and products and is widely used in the management domain. In this study, we apply classical PLC theory to mobile Apps on Apple smartphone and tablet devices (Apple App Store). Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of an App. We also use this ranking information to generate an App life cycle model. By using this model, we compare paid and free Apps from 20 different categories. Our results show that Apps across various categories have different kinds of life cycles and exhibit various unique and unpredictable characteristics. Furthermore, as large-scale heterogeneous data (e.g., user App ratings, App hardware/software requirements, or App version updates) become available and are attached to each target App, an important contribution of this paper is that we perform in-depth studies to explore how such data correlate and affect the App life cycle. Using different regression techniques (i.e., logistic, ordinary least squares, and partial least squares), we built different models to investigate these relationships. The results indicate that some explicit and latent independent variables are more important than others for the characterization of App life cycle. In addition, we find that life cycle analysis for different App categories requires different tailored regression models, confirming that inner-category App life cycles are more predictable and comparable than App life cycles across different categories.

Female directors and the cost of debt: does gender diversity in the boardroom matter to lenders?

2019 ◽  
Vol 34 (4) ◽  
pp. 374-392 ◽  
Author(s):  
Muhammad Usman ◽  
Muhammad Umar Farooq ◽  
Junrui Zhang ◽  
Muhammad Abdul Majid Makki ◽  
Muhammad Kaleem Khan
Keyword(s):  
Least Squares ◽  
Gender Diversity ◽  
Ordinary Least Squares ◽  
Cost Of Debt ◽  
Least Squares Regression ◽  
Two Stage ◽  
Loan Default ◽  
Content Type ◽  
Female Directors ◽  
The Cost

Purpose This paper aims to investigate the question concerning whether gender diversity in the boardroom matters to lenders or not? Design/methodology/approach To answer this question, the authors use the data from 2009 to 2015 of all A-share listed companies on the Shanghai and Shenzhen stock exchanges. The authors use ordinary least squares regression and firm fixed effect regression to draw our inferences. To check and control the issue of endogeneity the authors use one-year lagged gender diversity regression, two-stage least squares regression, propensity score matching method and Heckman two-stage regression. Findings The results suggest that the presence of female directors on the board reduces managerial opportunistic behavior and information asymmetry and, consequently, creditors’ perceptions about the probability of loan default and the cost of debt. The authors find that lenders charge 4 per cent less from borrowers that have at least one female board member than they do from borrowers with no female board members. The authors also find that the board structure (i.e. gender diversity) of government-owned firms also matters to lenders, as government-owned firms that have gender-diverse boards have a lower cost of debt (i.e. 5 per cent lower interest rate). Practical Implications The findings have implications for individual borrowers and for regulators. For example, borrowers can get debt financing at lower rates by altering their boards’ composition (i.e. through gender diversity). From the regulatory perspective, the results support recent legislative initiatives around the world regarding female directors’ representation on boards. Originality Value This paper makes several contributions. First, beyond the recent studies on boardroom gender, the authors investigate the relationship between gender diversity in the boardroom and the cost of debt. Second, the authors extend the literature on the association between government ownership and cost of debt by first time providing evidence that the board composition (e.g. gender diversity) of government-owned firms also matters to the lenders. The other contributions are discussed in the introduction section.

Improving hydrological forecasts through temporal hierarchal reconciliation

2021 ◽  
Author(s):  
Mohammad Sina Jahangir ◽  
John Quilty
Keyword(s):  
Least Squares ◽  
Performance Metrics ◽  
Moving Average ◽  
Weighted Least Squares ◽  
Absolute Error ◽  
Efficiency Index ◽  
Ordinary Least Squares ◽  
Data Driven ◽  
Wide Range ◽  
Different Levels

<p>Hydrological forecasts at different horizons are often made using different models. These forecasts are usually temporally inconsistent (e.g., monthly forecasts may not sum to yearly forecasts), which may lead to misaligned or conflicting decisions. Temporal hierarchal reconciliation (or simply, hierarchical reconciliation) methods can be used for obtaining consistent forecasts at different horizons. However, their effectiveness in the field of hydrology has not yet been investigated. Thus, this research assesses hierarchal reconciliation for precipitation forecasting due to its high importance in hydrological applications (e.g., reservoir operations, irrigation, drought and flood forecasting). Original precipitation forecasts (ORF) were produced using three different models, including ‘automatic’ Exponential Time-Series Smoothing (ETS), Artificial Neural Networks (ANN), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). The forecasts were produced at six timescales, namely, monthly, 2-monthly, quarterly, 4-monthly, bi-annual, and annual, for 84 basins selected from the Canadian model parameter experiment (CANOPEX) dataset. Hierarchical reconciliation methods including Hierarchical Least Squares (HLS), Weighted Least Squares (WLS), and Ordinary Least Squares (OLS) along with the Bottom-Up (BU) method were applied to obtain consistent forecasts at all timescales.</p><p>Generally, ETS and ANN showed the best and worst performance, respectively, according to a wide range of performance metrics (root mean square error (RMSE), normalized RMSE (nRMSE), mean absolute error (MAE), normalized MAE (nMAE), and Nash-Sutcliffe Efficiency index (NSE)). The results indicated that hierarchal reconciliation has a dissimilar impact on the ORFs’ accuracy in different basins and timescales, improving the RMSE in some cases while decreasing it in others. Also, it was highlighted that for different forecast models, hierarchical reconciliation methods showed different levels of performance. According to the RMSE and MAE, the BU method outperformed the hierarchical methods for ETS forecasts, while for ANN and SARIMA forecasts, HLS and OLS improved the forecasts more substantially, respectively. The sensitivity of ORF to hierarchical reconciliation was assessed using the RMSE. It was shown that both accurate and inaccurate ORF could be improved through hierarchical reconciliation; in particular, the effectiveness of hierarchical reconciliation appears to be more dependent on the ORF accuracy than it is on the type of hierarchical reconciliation method.</p><p>While in the present work, the effectiveness of hierarchical reconciliation for hydrological forecasting was assessed via data-driven models, the methodology can easily be extended to process-based or hybrid (process-based data-driven) models. Further, since hydrological forecasts at different timescales may have different levels of importance to water resources managers and/or policymakers, hierarchical reconciliation can be used to weight the different timescales according to the user’s preference/desired goals.</p>

scholarly journals From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

2013 ◽  
Vol 21 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
Marek Blazewicz ◽  
Ian Hinder ◽  
David M. Koppelman ◽  
Steven R. Brandt ◽  
Milosz Ciznicki ◽  
...  
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Large Scale ◽  
Einstein Equations ◽  
Efficient Manner ◽  
Code Transformations ◽  
Wide Range ◽  
Problem Description ◽  
Physics Model ◽  
High Level

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, theChemoraframework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

scholarly journals Does more maternal investment mean a larger brain? Evolutionary relationships between reproductive mode and brain size in chondrichthyans

2011 ◽  
Vol 62 (6) ◽  
pp. 567 ◽  
Author(s):  
Christopher G. Mull ◽  
Kara E. Yopak ◽  
Nicholas K. Dulvy
Keyword(s):  
Least Squares ◽  
Body Mass ◽  
Brain Size ◽  
Maternal Investment ◽  
Branch Length ◽  
Reproductive Mode ◽  
Ordinary Least Squares ◽  
Egg Laying ◽  
Wide Range ◽  
Relative Brain Size

Chondrichthyans have the most diverse array of reproductive strategies of any vertebrate group, ranging from egg-laying to live-bearing with placental matrotrophy. Matrotrophy is defined as additional maternal provisioning beyond the yolk to the developing neonate; in chondrichthyans, this occurs through a range of mechanisms including uterine milk, oophagy, uterine cannibalism and placentotrophy. Chondrichthyans also exhibit a wide range of relative brain sizes and highly diverse patterns of brain organisation. Brains are energetically expensive to produce and maintain, and represent a major energetic constraint during early life in vertebrates. In mammals, more direct maternal–fetal placental connections have been associated with larger brains (steeper brain–body allometric scaling relationships). We test for a relationship between reproductive mode and relative brain size across 85 species from six major orders of chondrichthyans by using several phylogenetic comparative analyses. Ordinary least-squares (OLS) and reduced major axis (RMA) regression of body mass versus brain mass suggest that increased maternal investment results in a larger relative brain size. Our findings were supported by phylogenetic generalised least-squares models (pGLS), which also highlighted that these results vary with evolutionary tempo, as described by different branch-length assumptions. Across all analyses, maximum body size had a significant influence on the relative brain size, with large-bodied species (body mass >100 kg) having relatively smaller brains. The present study suggests that there may be a link between reproductive investment and relative brain size in chondrichthyans; however, a more definitive test requires a better-resolved phylogeny and a more nuanced categorisation of the level of maternal investment in chondrichthyans.

Operational Redistribution Model for a Large-Scale Bicycle-Sharing System

2015 ◽  
Vol 2512 (1) ◽  
pp. 90-100 ◽  
Author(s):  
Mingzhu Song ◽  
Meng Li ◽  
Mingqiao Zou
Keyword(s):  
Large Scale ◽  
Optimal Number ◽  
Level Of Service ◽  
Penalty Cost ◽  
Operation Costs ◽  
User Demand ◽  
High Level ◽  
The Cost ◽  
Bicycle Sharing System ◽  
Detailed Work

In recent years, bicycle-sharing systems (BSSs) have been getting more and more popular in many cities all over the world, particularly in developing countries. However, a significant operating problem was the imbalance that occurred in the distribution of bicycles, especially in large-scale BSSs during peak hours. This problem could significantly reduce the level of service and number of potential users. To improve the level of service of BSSs, the necessity of redistribution was analyzed, and an operational redistribution model (ORM) that could deal with large-scale redistribution was developed. The objective of the ORM was to minimize the generalized operation costs of BSSs, which were the penalty cost of unserved user demand and the cost of redistribution of bicycles. The overall system performance was analyzed under several scenarios. The results demonstrated that an ORM could effectively improve the level of service of a BSS and could provide a detailed work plan for each redistribution truck to implement. For redistribution in a large-scale BSS, the partition of subzones was important to achieve a high level of service with relatively low generalized costs. In addition, an optimal number of subzones could be found through the scenario-based optimization process.

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