scholarly journals On accuracy of upper quantiles estimation

2010 ◽  
Vol 14 (11) ◽  
pp. 2167-2175 ◽  
Author(s):  
I. Markiewicz ◽  
W. G. Strupczewski ◽  
K. Kochanek
Keyword(s):  
Distribution Function ◽  
Population Distribution ◽  
Model Misspecification ◽  
Estimation Method ◽  
Flood Frequency ◽  
Likelihood Method ◽  
Flood Frequency Analysis ◽  
Estimation Methods ◽  
Annual Peak ◽  
Log Normal

Abstract. Flood frequency analysis (FFA) entails the estimation of the upper tail of a probability density function (PDF) of annual peak flows obtained from either the annual maximum series or partial duration series. In hydrological practice, the properties of various methods of upper quantiles estimation are identified with the case of known population distribution function. In reality, the assumed hypothetical model differs from the true one and one cannot assess the magnitude of error caused by model misspecification in respect to any estimated statistics. The opinion about the accuracy of the methods of upper quantiles estimation formed from the case of known population distribution function is upheld. The above-mentioned issue is the subject of the paper. The accuracy of large quantile assessments obtained from the four estimation methods is compared to two-parameter log-normal and log-Gumbel distributions and their three-parameter counterparts, i.e., three-parameter log-normal and GEV distributions. The cases of true and false hypothetical models are considered. The accuracy of flood quantile estimates depends on the sample size, the distribution type (both true and hypothetical), and strongly depends on the estimation method. In particular, the maximum likelihood method loses its advantageous properties in case of model misspecification.

scholarly journals On accuracy of upper quantiles estimation

2010 ◽  
Vol 7 (4) ◽  
pp. 4761-4784
Author(s):  
I. Markiewicz ◽  
W. G. Strupczewski ◽  
K. Kochanek
Keyword(s):  
Distribution Function ◽  
Population Distribution ◽  
Model Misspecification ◽  
Estimation Method ◽  
Flood Frequency ◽  
Likelihood Method ◽  
Flood Frequency Analysis ◽  
Estimation Methods ◽  
Hypothetical Model ◽  
Log Normal

Abstract. Flood frequency analysis (FFA) entails estimation of the upper tail of a probability density function (PDF) of annual peak flows obtained from either the annual maximum series or partial duration series. In hydrological practice the properties of various estimation methods of upper quantiles are identified with the case of known population distribution function. In reality the assumed hypothetical model differs from the true one and one can not assess the magnitude of error caused by model misspecification in respect to any estimated statistics. The opinion about the accuracy of the methods of upper quantiles estimation formed from the case of known population distribution function is upheld. The above-mentioned issue is the subject of the paper. The accuracy of large quantile assessments obtained from the four estimation methods are compared for two-parameter log-normal and log-Gumbel distributions and their three-parameter counterparts, i.e., three-parameter log-normal and GEV distributions. The cases of true and false hypothetical model are considered. The accuracy of flood quantile estimates depend on the sample size, on the distribution type, both true and hypothetical, and strongly depend on the estimation method. In particular, the maximum likelihood method looses its advantageous properties in case of model misspecification.

scholarly journals Using comparative analysis to teach about the nature of nonstationarity in future flood predictions

2012 ◽  
Vol 16 (5) ◽  
pp. 1269-1279 ◽  
Author(s):  
S. B. Shaw ◽  
M. T. Walter
Keyword(s):  
Comparative Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Atmospheric Rivers ◽  
Changing Climate ◽  
Annual Peak ◽  
The Us ◽  
Hydrologic System ◽  
Daily Flows

Abstract. Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students' insights into the degree and predictability of future non-stationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics.

scholarly journals Flood frequency analysis using mean daily flows vs. instantaneous peak flows

2021 ◽  
Author(s):  
Anne Bartens ◽  
Uwe Haberlandt
Keyword(s):  
Frequency Analysis ◽  
Linear Models ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Estimation Methods ◽  
Entire Study ◽  
Flood Peak ◽  
Flood Dynamics ◽  
Event Based ◽  
Available Information

Abstract. In many cases flood frequency analysis needs to be carried out on mean daily flow (MDF) series without any available information on the instantaneous peak flow (IPF). We analyze the error of using MDFs instead of IPFs for flood quantile estimation on a German dataset and assess spatial patterns and factors that influence the deviation of MDF floods from their IPF counterparts. The main dependence could be found for catchment area but also gauge elevation appeared to have some influence. Based on the findings we propose simple linear models to correct both MDF flood peaks of individual flood events and overall MDF flood statistics. Key predictor in the models is the event-based ratio of flood peak and flood volume obtained directly from the daily flow records. This correction approach requires a minimum of data input, is easily applied, valid for the entire study area and successfully estimates IPF peaks and flood statistics. The models perform particularly well in smaller catchments, where other IPF estimation methods fall short. Still, the limit of the approach is reached for catchment sizes below 100 km2, where the hydrograph information from the daily series is no longer capable of approximating instantaneous flood dynamics.

Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions

2020 ◽  
Vol 45 (4) ◽  
pp. 475-506 ◽  
Author(s):  
Soojin Park ◽  
Gregory J. Palardy
Keyword(s):  
Compliance Rate ◽  
Estimation Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Randomized Experiments ◽  
Distributional Assumption ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Sensitivity Evaluation ◽  
Mediating Mechanisms

Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However, little research has examined their performance when certain assumptions are violated and under varying data conditions. This article addresses that gap in the research and compares the performance of the two methods. The results show that the distributional assumption of the compliance behavior plays an important role in estimation. That is, regardless of the estimation method or whether the other assumptions hold, results are biased if the distributional assumption is not met. We also found that the IV-mediate method is more sensitive to exclusion restriction violations, while the ML-mediate method is more sensitive to monotonicity violations. Moreover, estimates depend in part on compliance rate, sample size, and the availability and impact of control covariates. These findings are used to provide guidance on estimator selection.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

scholarly journals Regional Flood Frequency Analysis of the Pannonian Basin

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 193 ◽  
Author(s):  
Igor Leščešen ◽  
Dragan Dolinaj
Keyword(s):  
Frequency Analysis ◽  
Pannonian Basin ◽  
Regional Distribution ◽  
Threshold Level ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Regional Flood Frequency Analysis ◽  
Log Normal ◽  
Best Fit ◽  
Regional Flood Frequency

In this paper, we performed Regional Flood Frequency Analysis (RFFA) by using L-moments and Annual Maximum Series (AMS) methods. Time series of volumes and duration of floods were derived using the threshold level method for 22 hydrological stations in the Pannonian Basin. For flood definition, a threshold set at Q10 was used. The aim of this research is to derive best-fit regional distribution for the four major rivers within the Pannonian Basin and to provide reliable prediction of flood quantiles. The results show that the investigated area can be considered homogeneous (Vi < 1) both for flood volumes (0.097) and durations (0.074). To determine the best-fit regional distribution, the six most commonly used distributions were used. Results obtained by L-moment ratio diagram and Z statistics show that all distributions satisfy the test criteria, but because the Log-Normal distribution has the value closest to zero, it can be selected as the best-fit distribution for the volumes (0.12) and durations (0.25) of floods.

scholarly journals Sources of Error in IRT Trait Estimation

2017 ◽  
Vol 42 (5) ◽  
pp. 359-375 ◽  
Author(s):  
Leah M. Feuerstahler
Keyword(s):  
Item Response ◽  
Latent Trait ◽  
Model Misspecification ◽  
Estimation Method ◽  
Item Parameter ◽  
Estimation Methods ◽  
Trait Score ◽  
Confidence Interval Coverage ◽  
Coverage Rates ◽  
Trait Estimation

In item response theory (IRT), item response probabilities are a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (a) random error, (b) the trait score estimation method, (c) errors in item parameter estimation, and (d) model misspecification. This study investigated the relative effects of these error sources on the bias and confidence interval coverage rates for trait scores. Our results showed that overall, bias values were close to 0, and coverage rates were fairly accurate for central trait scores and trait estimation methods that did not use a strong Bayesian prior. However, certain types of model misspecifications were found to produce severely biased trait estimates with poor coverage rates, especially at extremes of the latent trait continuum. It is demonstrated that biased trait estimates result from estimated item response functions (IRFs) that exhibit systematic conditional bias, and that these conditionally biased IRFs may not be detected by model or item fit indices. One consequence of these results is that certain types of model misspecifications can lead to estimated trait scores that are nonlinearly related to the data-generating latent trait. Implications for item and trait score estimation and interpretation are discussed.

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