Sample size for comparison of changes in the presence of right censoring caused by death, withdrawal, and staggered entry
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Censoring (clinical trials)
Ordinary least squares
Ordinary least squares
Instrumental variable
Omitted-variable bias
Variables
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주택시장 분석에 널리 사용되는 헤도닉 방법은 OLS(ordinary least squares) 모형을 이용하는데, 이는 오차가 독립적이며, 평균이 0이고, 분산이 일정하다는 가정에 기초한다. 그러나 공간 자기상관이 존재할 경우에는 이러한 가정에 위배되며, 공간효과를 제대로 반영하지 않으면 왜곡된 추정결과를 가져오게 된다. 최근 이에 대한 대안으로 공간계량모형이 도입되고 있는데, 이 연구에서는 OLS 모형과 공간계량모형의 적합도를 비교 평가하고자 한다. 부산시 실거래 주택매매 가격자료를 이용하여 분석한 결과, OLS를 이용한 기존의 헤도닉 모형보다는 공간자기상관을 고려한 공간계량모형들이 보다 설명력이 높았다. Dubin이 제시한 기준과 Log Likelihood 기준을 통해 볼 때 공간계량모형 중에서는 공간자기회귀모형(spatial autoregressive model: SAR)모형의 적합도가 높은 것으로 나타났다. 이를 통해 주택가격에 있어서의 공간효과를 확인할 수 있었으며, 재건축 추진여부가 아파트 매매가격에 매우 큰 영향을 미침을 알 수 있었다. 또한 적절한 공간계량모형의 선택은 정부의 주택정책에 있어서도 매우 중요하다고 하겠다. The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$ ). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.
Ordinary least squares
Econometric model
Spatial Econometrics
Spatial Dependence
Goodness of fit
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The present study compares the Fama-French three factor coefficient estimates obtained from both ordinary least squares (OLS) and quantile regression for 25 size-value sorted portfolios of BSE 500. The study, using empirical results, residual graphs and other plots, confirms the inefficiency of OLS in end distribution estimation. Quantile regression reveals that the slope direction for all coefficients of predictor variables is not the same across the quantiles and time. Finally, the study shows, empirically, that quantile regression estimates give a more comprehensive and clearer picture of the varying effect of predictors on response variables to analysts or investors in making investment decisions.
Quantile regression
Ordinary least squares
Quantile
Investment
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Sample (material)
Nominal level
Sampling distribution
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The effect of the training dataset sample size has been shown to have profound outcomes on the performance of species distribution models. However, the effects that the testing dataset sample size can have on the assessment of a models predictive capacity has received little attention. In this study, I used simulations to study how accurate two discrimination statics, the AUC (the area under the receiver operating characteristic – ROC – curve) and Se* (the probability of correctly classifying any case and calculated from the threshold that makes minimum the difference between sensitivity and specificity), are estimated based on sample size. ROC curves with known discrimination ability were simulated, samples were randomly taken, the two discrimination statistics were estimated, and the differences between the two estimators and their respective true values were computed to understand how bias and precision were affected by sample size. In general, as sample size increases, the difference between reported and true discrimination capacity decreased. There were no important differences between the estimated AUC and Se* statistics in terms of bias and precision. Under realistic scenarios where the ROC points are not necessarily part of the true underlying ROC curve, the two discrimination statistics are both unbiased and equally precise, and the higher the true discrimination capacity is, the more accurate they are estimated. Between 20 and 30 is a lowest sample size limit since below this interval accuracy estimates considerably decreases. All together, these results are very important since many interesting SDM applications involve rare and poorly known species for which sample sizes are unavoidably small.
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Testing hypotheses or evaluation confidence intervals requires knowledge of some statistics’ distributions. It is convenient if the probability distribution of the statistic converges to normal distribution when the sample size is sufficiently large. This paper examines the problem of how to evaluate sample size in order to determine that a statistic’s distribution does not depart from normal distribution by more than an assumed amount. Two procedures are proposed to evaluate the necessary sample size. The first is based on Berry-Esseen inequality while the second is based on simulation procedure. In order to evaluate the necessary sample size, the distribution of the sample mean is generated by replicating samples of a fixed size. Next, the normal distribution of the evaluated sample means is tested. The size of the generated samples is gradually increased until the hypothesis on the normality of the sample mean distribution is not rejected. This procedure is applied in the cases of statistics other than sample mean.
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Sampling distribution
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A simulation study was performed to examine the effects of withdrawal censoring and fixed right censoring on the level of significance and power of two-sample tests. Statistics based on Gehan, logrank, and Peto-Prentice scores are used. It is demonstrated that the performance of these tests can be adversely affected by a high percenage of withdrawals and/or unequal sample sizes
Censoring (clinical trials)
Log-rank test
Sample (material)
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Ordinary least squares econometric approaches to estimating election vote outcomes potentially ignore spatial dependence (or autocorrelation) in the data that may affect estimates of voting behavior. The presence of spatial autocorrelation in the data can yield biased or inconsistent point estimates when ordinary least squares is used inappropriately. Therefore, this paper puts forward a spatial econometric model to estimate the vote outcomes in the 2004 presidential election. We contribute to the literature in two ways. One, we extend the voting behavior literature by considering newly developed spatial specification tests to determine the proper econometric model. The results of two different spatial specification tests suggest that a spatial Durbin model provides a better fit to the data. Two, we offer a richer interpretation of the spatial effects, which differ from standard ordinary least squares estimates, of the county-level vote outcome for the 2004 presidential election.
Ordinary least squares
Spatial Econometrics
Presidential election
Econometric model
Specification
Least-squares function approximation
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Our constitution fails to provide a legal system for censoring the cases against the constitution. It is, therefore, necessary to set up one to stipulate clearly and systematically the censoring contents, procedures, mode and effectiveness as well as institutions for censoring, objects to be censored, and the way of bearing responsibilities.
Censoring (clinical trials)
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Abstract Fixed transactions costs that prohibit exchange engender bias in supply analysis due to censoring of the sample observations. The associated bias in conventional regression procedures applied to censored data and the construction of robust methods for mitigating bias have been preoccupations of applied economists since Tobin [Econometrica 26 (1958) 24]. This literature assumes that the true point of censoring in the data is zero and, when this is not the case, imparts a bias to parameter estimates of the censored regression model. We conjecture that this bias can be significant; affirm this from experiments; and suggest techniques for mitigating this bias using Bayesian procedures. The bias‐mitigating procedures are based on modifications of the key step that facilitates Bayesian estimation of the censored regression model; are easy to implement; work well in both small and large samples; and lead to significantly improved inference in the censored regression model. These findings are important in light of the widespread use of the zero‐censored Tobit regression and we investigate their consequences using data on milk‐market participation in the Ethiopian highlands.
Censoring (clinical trials)
Tobit model
Censored regression model
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