The Theoretical Relationship between Sample Size and Expected Predictive Precision for EQ-5D Valuation Studies: A Mathematical Exploration and Simulation Study

2020 ◽  
Vol 40 (3) ◽  
pp. 339-347 ◽  
Author(s):  
Kelvin K. W. Chan ◽  
Eleanor M. Pullenayegum
Keyword(s):  
Sample Size ◽  
Least Squares ◽  
Closed Form ◽  
Random Effects ◽  
Simulation Study ◽  
Ordinary Least Squares ◽  
Health States ◽  
Data Set ◽  
The Mean ◽  
Closed Form Formula

Background. Scoring algorithms of multi-attribute utility instruments (MAUI) are developed in valuation studies and are hence estimated subject to uncertainty. Valuation studies need to be designed to achieve reasonable accuracy. We aim to provide the first closed-form mathematical formula for the mean square error (MSE) of an additive MAUI as a function of sample size that acknowledges that the MAUI model for the mean utility is not a perfect fit. Methods. Based on the design of the EQ-5D valuation study, we derived our closed-form formula in terms of sample size and number of directly valued health states overall and per subject. We validated our formula by conducting a simulation study using the US EQ-5D-3L valuation data set and examined the effect of using a random-effects versus an ordinary least-squares model and the effect of heteroscedasticity. We explored the effect of sample size and number of valued health states. Results. The simulation study validated our MSE-based closed-form formula regardless of whether assuming a random-effects model versus an ordinary least squares model or heteroscedasticity versus homoscedasticity. As the sample size approaches infinity, the MSE does not approach zero but levels off asymptotically. The improvement based on increasing sample is more prominent when the sample is small. When the sample size is greater than 300 to 500, further increases do not meaningfully improve the MSE, while increasing the number of health states can further improve the MSE. Conclusion. We have derived a closed-form formula to calculate the MSE of an additive MAUI scoring algorithm based on sample size and number of health states, which will enable the developers of MAUI valuation studies to calculate the required sample size for their desired predictive precision.

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.

A DETERMINISTIC METHODOLOGY FOR ESTIMATION OF PARAMETERS IN DYNAMIC MARKOV CHAIN MODELS

2011 ◽  
Vol 19 (01) ◽  
pp. 71-100 ◽  
Author(s):  
A. R. ORTIZ ◽  
H. T. BANKS ◽  
C. CASTILLO-CHAVEZ ◽  
G. CHOWELL ◽  
X. WANG
Keyword(s):  
Inverse Problem ◽  
Markov Chain ◽  
Least Squares ◽  
Large Population ◽  
Ordinary Least Squares ◽  
Large Sample Size ◽  
Data Set ◽  
Markov Chain Models ◽  
Sample Paths ◽  
Dynamic Stochastic

A method for estimating parameters in dynamic stochastic (Markov Chain) models based on Kurtz's limit theory coupled with inverse problem methods developed for deterministic dynamical systems is proposed and illustrated in the context of disease dynamics. This methodology relies on finding an approximate large-population behavior of an appropriate scaled stochastic system. The approach leads to a deterministic approximation obtained as solutions of rate equations (ordinary differential equations) in terms of the large sample size average over sample paths or trajectories (limits of pure jump Markov processes). Using the resulting deterministic model, we select parameter subset combinations that can be estimated using an ordinary-least-squares (OLS) or generalized-least-squares (GLS) inverse problem formulation with a given data set. The selection is based on two criteria of the sensitivity matrix: the degree of sensitivity measured in the form of its condition number and the degree of uncertainty measured in the form of its parameter selection score. We illustrate the ideas with a stochastic model for the transmission of vancomycin-resistant enterococcus (VRE) in hospitals and VRE surveillance data from an oncology unit.

Determinants of SME growth

2016 ◽  
Vol 39 (9) ◽  
pp. 966-986 ◽  
Author(s):  
Habib Kachlami ◽  
Darush Yazdanfar
Keyword(s):  
Random Effects ◽  
Financial Institutions ◽  
Significant Influence ◽  
Fixed Effects ◽  
Explanatory Power ◽  
Ordinary Least Squares ◽  
Firm Level ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to study the firm-level financial variables affecting the growth of small and medium-sized enterprises (SMEs). Design/methodology/approach The study applies a resource-based view to analyze the firm-level as well as industry-level determinants of SME growth. Empirical evidence has also been provided from a data set of SMEs in Sweden to support the hypotheses. For a robust statistical analysis, three models – ordinary least squares (OLS) regression, random-effects regression and fixed-effects regression – are used to examine the influence of explanatory variables on growth. Findings The findings of this study show a positive and significant influence of profitability, short-term debt and size on a firm’s growth across all three models. Results regarding the influence of long-term debt on growth, however, are mixed. While the results of a fixed-effect model show the negative and significant influence of long-term debt on growth, the results according to OLS and random effects show long-term debt positively related to growth. Research limitations/implications This study has been conducted over a period of four years and in the context of Sweden which may limit the generalizability of its results for longer periods and for different contexts. Moreover, the low explanatory power of the models implies the need to also consider other types of variables, such as managerial or socio-economic variables, to better explain the determinants of SME growth. Practical implications Understanding the determinants of growth can be important for policy makers, SME managers and financial institutions. The findings of this study can be used for designing policies which stimulate SME growth. Realizing the financial resources that influence growth can also help SME managers and financial institutions to understand each other’s need for better cooperation. Originality/value This paper applies different models for analyzing large and cross-sectoral data regarding SME growth in the context of Sweden.

scholarly journals Comparison of Competing Approaches to Analyzing Cross-Classified Data: Random Effects Models, Ordinary Least Squares, or Fixed Effects with Cluster Robust Standard Errors

2021 ◽  
Author(s):  
Young Ri Lee ◽  
James E Pustejovsky
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Good Alternative ◽  
Ordinary Least Squares ◽  
Alternative Methods ◽  
Least Squares Regression ◽  
Monte Carlo Simulation Study ◽  
Random Effects Models ◽  
Fixed Effects Regression

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in education. However, when the focus of a study is on the regression coefficients at level one rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods may be advantageous because they rely on weaker assumptions than what is required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models with crossed random effects, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated. We found that CCREM performed the best when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. FE-CRVE showed the best performance when the exogeneity assumption is violated. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt.

scholarly journals Effects of environmental factors on multiple ovulation of zebu donors

2006 ◽  
Vol 58 (4) ◽  
pp. 567-574 ◽  
Author(s):  
M.G.C.D. Peixoto ◽  
J.A.G. Bergmann ◽  
C.G. Fonseca ◽  
V.M. Penna ◽  
C.S. Pereira
Keyword(s):  
Environmental Factors ◽  
Least Squares ◽  
Inbreeding Coefficient ◽  
Corpora Lutea ◽  
Data Sets ◽  
Data Set ◽  
The Third ◽  
Multiple Ovulation ◽  
The Mean ◽  
Best Responses

Data on 1,294 superovulations of Brahman, Gyr, Guzerat and Nellore females were used to evaluate the effects of: breed; herd; year of birth; inbreeding coefficient and age at superovulation of the donor; month, season and year of superovulation; hormone source and dose; and the number of previous treatments on the superovulation results. Four data sets were considered to study the influence of donors’ elimination effect after each consecutive superovulation. Each one contained only records of the first, or of the two firsts, or three firsts or all superovulations. The average number of palpated corpora lutea per superovulation varied from 8.6 to 12.6. The total number of recovered structures and viable embryos ranged from 4.1 to 7.3 and from 7.3 to 13.8, respectively. Least squares means of the number of viable embryos at first superovulation were 7.8 ± 6.6 (Brahman), 3.7 ± 4.5 (Gyr), 6.1 ± 5.9 (Guzerat) and 5.2 ± 5.9 (Nellore). The numbers of viable embryos of the second and the third superovulations were not different from those of the first superovulation. The mean intervals between first and second superovulations were 91.8 days for Brahman, 101.8 days for Gyr, 93.1 days for Guzerat and 111.3 days for Nellore donors. Intervals between the second and the third superovulations were 134.3, 110.3, 116.4 and 108.5 days for Brahman, Gyr, Guzerat and Nellore donors, respectively. Effects of herd nested within breed and dose nested within hormone affected all traits. For some data sets, the effects of month and order of superovulation on three traits were importants. The maximum number of viable embryos was observed for 7-8 year-old donors. The best responses for corpora lutea and recovered structures were observed for 4-5 year-old donors. Inbreeding coefficient was positively associated to the number of recovered structures when data set on all superovulations was considered.

scholarly journals Impact of Full IFRS, Accounting Standards for SMES and Company Demographics on Firms’ Return on Asset and Return on Equity using Panel Data Regression

2019 ◽  
Vol 8 (6S) ◽  
pp. 589-601 ◽  
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Ordinary Least Squares ◽  
Lagrangian Multiplier ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Real Property ◽  
The Impact

Due to globalization, markets are becoming more interconnected as the companies are engaged in doing cross-border offerings. Currently, competitions are intensified because Domestic organizations discover themselves competing with each nearby opposite numbers and worldwide companies. But one component that hinders SMEs is the need for reliable and similar monetary data. According to Abarca (2014), adoption of a high-quality and consistent set of accounting requirements is critical so as for the businesses to remain competitive in ASEAN member states. This paper ambitions to answer the query, what modified into the extent of the impact of compliance with full IFRS and IFRS for SMEs on profitability of agencies belong to real property enterprise? This paper moreover sought to decide whether there may be a sizeable distinction among the groups’ compliance with the overall PFRS and the PFRS for SMEs and to determine whether or now not there is a massive distinction among the companies’ financial normal overall performance earlier than and after the adoption of the PFRS for SMEs.Paired T-test have become employed in case you need to determine whether there is a big distinction between the agencies’ compliance with the entire PFRS and the PFRS for SMEs and to decide whether or not there may be a big difference some of the groups’ monetary performance earlier than and after the adoption of the PFRS for SMEs. Using STATA, the great appropriate version for every economic ratio on the subject of degree of compliance emerge as determined on. First, take a look at parm command became used to find out which most of the Least Squares Dummy Variable Regression Modes (LSDV1, LSDV2, LSDV3) underneath the Fixed Effects Model is the ideal version. Afterwards, Hausman Fixed Random Test changed into used to pick out out which is more suitable amongst Fixed Effects Model and Random Effects Model. If Fixed Effects Model modified into the more appropriate one, the Wald’s test turn out to be used to determine the best version among Fixed Effects Model and Ordinary Least Squares Model. On the alternative hand, if Random Effects Model became the more suitable one, the Breusch and Pagan Lagrangian Multiplier Test for Random Effect have become used to decide the satisfactory version amongst Random Effects Model and Ordinary Least Squares. Moreover, if Ordinary Least Squares became the splendid model, it is going to be in addition tested to check for heteroscedasticity and multicollinearity. White’s test became used to check for heterescedasticity and Variance Inflation Factor have become used to test if multicollinearity is gift. The results display that the adoption of PFRS for SMEs stepped forward the compliance of Philippine real property SMEs. However, no vast alternate became said inside the financial average performance of those companies (as measured with the resource of cross back on assets and go back on equity). This was further supported by the results of the panel regression. This means that despite having a relatively

scholarly journals THE EFFECT OF URBAN GREEN SPACES ON HOUSE PRICES IN WARSAW

2018 ◽  
Vol 22 (5) ◽  
pp. 358-371 ◽  
Author(s):  
Radoslaw Trojanek ◽  
Michal Gluszak ◽  
Justyna Tanas
Keyword(s):  
Least Squares ◽  
Residential Buildings ◽  
House Prices ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Data Set ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Green Area ◽  
The Impact

In the paper, we analysed the impact of proximity to urban green areas on apartment prices in Warsaw. The data-set contained in 43 075 geo-coded apartment transactions for the years 2010 to 2015. In this research, the hedonic method was used in Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and Median Quantile Regression (Median QR) models. We found substantial evidence that proximity to an urban green area is positively linked with apartment prices. On an average presence of a green area within 100 meters from an apartment increases the price of a dwelling by 2,8% to 3,1%. The effect of park/forest proximity on house prices is more significant for newer apartments than those built before 1989. We found that proximity to a park or a forest is particularly important (and has a higher implicit price as a result) in the case of buildings constructed after 1989. The impact of an urban green was particularly high in the case of a post-transformation housing estate. Close vicinity (less than 100 m distance) to an urban green increased the sales prices of apartments in new residential buildings by 8,0–8,6%, depending on a model.

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