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 Title
 Assessing Crop Yield Simulations Driven By The Narccap Regional Climate Models In The Southeast United States.
 Creator

Shin, D. W., Baigorria, Guillermo A., Romero, Consuelo C., Cocke, Steve, Oh, JiHyun, Kim, BaekMin
 Abstract/Description

A set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/ cotton yield amounts for the period of 19812003 are obtained in a regularly gridded (similar to 20km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel...
Show moreA set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/ cotton yield amounts for the period of 19812003 are obtained in a regularly gridded (similar to 20km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regionalclimate modeldriven crop yield amounts are better simulated than the reanalysisdriven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The biascorrected or weighted composite methods combine the crop yield ensemble members better than the simple compositemethod. In general, the weighted ensemble crop yield simulations match marginally better with the observedweatherdriven yields compared to those of the other ensemble methods.
Show less  Date Issued
 20170316
 Identifier
 FSU_libsubv1_wos_000398064200002, 10.1002/2016JD025576
 Format
 Citation
 Title
 Distributed knowledge in an online patient support community: Authority and discovery.
 Creator

Kazmer, Michelle M., Lustria, Mia, Cortese, Juliann, Burnett, Gary, Kim, JiHyun, Ma, Jinxuan, Frost, Jeana
 Date Issued
 2014
 Identifier
 FSU_migr_slis_faculty_publications0012, 10.1002/asi.23064
 Format
 Citation
 Title
 Conditional bootstrap methods for censored data.
 Creator

Kim, JiHyun., Florida State University
 Abstract/Description

We first consider the random censorship model of survival analysis. The pairs of positive random variables ($X\sb{i},Y\sb{i}$), i = 1,$\...$,n, are independent and identically distributed, with distribution functions F(t) = P($X\sb{i} \leq\ t$) and G(t) = P($Y\sb{i} \leq\ t$) and the Y's are independent of the X's. We observe only ($T\sb{i},\delta\sb{i}$), i = 1,$\...$,n, where $T\sb{i}$ = min($X\sb{i},Y\sb{i}$) and $\delta\sb{i}$ = I($X\sb{i} \leq\ Y\sb{i}$). The X's represent survival times...
Show moreWe first consider the random censorship model of survival analysis. The pairs of positive random variables ($X\sb{i},Y\sb{i}$), i = 1,$\...$,n, are independent and identically distributed, with distribution functions F(t) = P($X\sb{i} \leq\ t$) and G(t) = P($Y\sb{i} \leq\ t$) and the Y's are independent of the X's. We observe only ($T\sb{i},\delta\sb{i}$), i = 1,$\...$,n, where $T\sb{i}$ = min($X\sb{i},Y\sb{i}$) and $\delta\sb{i}$ = I($X\sb{i} \leq\ Y\sb{i}$). The X's represent survival times, the Y's represent censoring times. Efron (1981) proposed two bootstrap methods for the random censorship model and showed that they are distributionally the same. Akritas (1986) established the weak convergence of the bootstrapped KaplanMeier estimator of F when bootstrapping is done by this method. Let us now consider bootstrapping more closely. Suppose that we wish to estimate the variance of F(t). If we knew the Y's then we would condition on them by the ancillarity principle, since the distribution of the Y's does not depend on F. That is, we would want to estimate Var$\{$F(t)$\vert Y\sb1,\...,Y\sb{n}\}$. Unfortunately, in the random censorship model we do not see all the Y's. If $\delta\sb{i}$ = 0 we see the exact value of $Y\sb{i}$, but if $\delta\sb{i}$ = 1 we know only that $Y\sb{i} > T\sb{i}$. Let us denote this information on the Y's by ${\cal C}$. Thus, what we want to estimate is Var$\{$F(t)$\vert{\cal C}\}$. Efron's scheme is appropriate for estimating the unconditional variance. We propose a new bootstrap method which provides an estimate of Var$\{$F(t)$\vert{\cal C}\}$., In this research we show that the KaplanMeier estimator of F formed by the new bootstrap method has the same limiting distribution as the one by Efron's approach. The results of simulation studies assessing the small sample performance of the two bootstrap methods are reported. We also consider the model in which the $X\sb{i}$'s are censored by the $Y\sb{i}$'s and also by known fixed constants, and propose an appropriate bootstrap method for that model. This bootstrap method is a readily modified version of the new bootstrap method above.
Show less  Date Issued
 1990, 1990
 Identifier
 AAI9113938, 3162201, FSDT3162201, fsu:78399
 Format
 Document (PDF)