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A new confidence interval for the odds ratio

100%
Zielińska-Kolasińska Z., Zieliński W.
Statistics in Transition new series
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2023
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vol. 24
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issue 2
113-128
EN
We consider the problem of interval estimation of the odds ratio. An asymptotic confidence interval is widely applied in economics, medicine, sociology, etc. Unfortunately, this confidence interval has a poor coverage probability, significantly smaller than the nominal confidence level. In this paper, a new confidence interval is proposed. Its construction requires only information on the sizes of samples and the sample odds ratio. The coverage probability of the proposed confidence interval is at least the nominal confidence level.
2
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A CONFIDENCE INTERVAL FOR PROPORTION IN FINITE POPULATION DIVIDED INTO TWO STRATA: A NUMERICAL STUDY

80%
Zieliński W.
Metody Ilościowe w Badaniach Ekonomicznych
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2015
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vol. 16
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issue 2
152-158
EN
Consider a finite population of N units. Let θє(0,1) denotes the fraction of units with a given property. The problem is in interval estimation of θ on the basis of a sample drawn due to the simple random sampling without replacement. Suppose, that the population is divided into two (disjoint) strata. In the paper the confidence interval for θ is proposed based on samples from two strata.
3
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CONFIDENCE INTERVALS FOR FRACTION IN FINITE POPULATIONS: MINIMAL SAMPLE SIZE

80%
Zieliński W.
Metody Ilościowe w Badaniach Ekonomicznych
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2013
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vol. 14
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issue 2
309-315
EN
Consider a finite population of N units. Let θ∈(0,1) denotes the fraction of units with a given property. The problem is in interval estimation of θ on the basis of a sample drawn due to the simple random sampling without re¬pla¬ce¬ment. It is of interest to obtain confidence intervals of a prescribed length. In the paper the minimal sample size which guarantees the length to not exceed the given value is calculated.
4
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Robustness of The Confidence Interval for At-Risk-Of-Poverty-Rate

80%
Zieliński W.
Statistics in Transition new series
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2011
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vol. 12
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issue 1
115-126
EN
Zieliński (2009) constructed a nonparametric interval for At-Risk-of-Poverty-Rate. It appeared that the confidence level of the interval depends on the underlying distribution of the income. For some distributions (e.g. lognormal, gamma, Pareto) the confidence level may be smaller than the nominal one. The question is, what is the largest deviance from the nominal level? In the paper, a more general problem is considered, i.e. the problem of robustness of the confidence level of the confidence interval for binomial probability. The worst distribution is derived as well as the smallest true confidence level is calculated. Some asymptotic remarks (sample size tends to infinity) are also given.
5
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Estimating the confidence interval of the regression coefficient of the blood sugar model through a multivariable linear spline with known variance

80%
Islamiyati A., Raupong R., Kalondeng A., Sari U.
Statistics in Transition new series
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2022
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vol. 23
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issue 1
201-212
EN
Estimates from confidence intervals are more powerful than point estimates, because there are intervals for parameter values used to estimate populations. In relation to global conditions, involving issues such as type 2 diabetes mellitus, it is very difficult to make estimations limited to one point only. Therefore, in this article, we estimate confidence intervals in a truncated spline model for type 2 diabetes data. We use a non-parametric regression model through a multi-variable spline linear estimator. The use of the model results from the irregularity of the data, so it does not form a parametric pattern. Subsequently, we obtained the interval from beta parameter values for each predictor. Body mass index, HDL cholesterol, LDL cholesterol and triglycerides all have two regression coefficients at different intervals as the number of the found optimal knot points is one. This value is the interval for multivariable spline regression coefficients that can occur in a population of type 2 diabetes patients.
6
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On some confidence intervals for population mean in case of asymmetric distributions of random variables

80%
Baszczyńska A., Pekasiewicz D., University of Łódź C. o. S. M.
Acta Universitatis Lodziensis. Folia Oeconomica
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2008
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vol. 216
EN
In the paper we present some methods of interval estimation of the population mean of skewed population. We consider nonparametric estimation method where information about the value of asymmetry coefficient is used. We apply simulation methods to compare the lengths of confidence intervals obtained by the considered method and the classical one.
PL
W pracy przedstawiono pewną metodę estymacji przedziałowej średniej dla populacji o asymetrycznym rozkładzie. Rozważano metodę nieparametryczną wykorzystującą informacje o rzeczywistej lub oszacowanej wartości współczynnika asymetrii populacji. Za pomocą metod symulacyjnych dokonano porównania rozważanej metody z metodą klasyczną poprzez analizę długości przedziałów ufności oraz analizę odsetka przedziałów pokrywających szacowany parametr.
7
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Bootstrap Confidence Intervals for Population Mean in the Case of Asymmetric Distributions of Random Variables

80%
Baszczyńska A., Pekasiewicz D., University of Łódź C. o. S. M.
Acta Universitatis Lodziensis. Folia Oeconomica
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2008
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vol. 216
PL
W pracy przedstawiono wybrane metody bootstrapowe estymacji przedziałowej wartości oczekiwanej populacji o rozkładzie asymetrycznym. Rozważano standardową metodę bootstrapowa, metodę percentyli oraz metodę t-bootstrapową. Metody te można stosować przy estymacji wartości oczekiwanej zmiennej losowej o rozkładzie asymetrycznym, zarówno dla małych jak i dużych prób. Analiza własności bootstrapowych metod estymacji przedziałowej przeprowadzona została metodami Monte Carlo.
EN
In the paper we present some chosen bootstrap methods of interval estimation of the population expectation for asymmetric distribution. We consider the standard bootstrap method, percentile method and t-bootstrap method. These methods can be used to estimate the expected value of asymmetric distribution for, both, small and large sample sizes. The analysis of the properties of bootstrap methods of interval estimation is performed by means of a simulation experiment.
8
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Comparison of confidence intervals for fraction in finite populations

70%
Zieliński W.
Metody Ilościowe w Badaniach Ekonomicznych
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2011
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vol. 12
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issue 1
177-182
EN
Consider a finite population. Let qÎ(0,1) denotes the fraction of units with a given property. The problem is in interval estimation of q on the basis of a sample drawn due to the simple random sampling without replacement. In the paper three confidence intervals are compared: exact based on hypergeometric distribution and two other based on approximations to hypergeometric distribution: Binomial and Normal. It appeared that Binomial based confidence interval is too conservative while the Normal based one does not keep the prescribed confidence level.
9
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Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

70%
Thangjai W., Niwitpong S.
Statistics in Transition new series
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2020
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vol. 21
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issue 5
41-60
XX
Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.
10
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Forecasting the confidence interval of efficiency in fuzzy DEA

61%
KAFI A., DANESHIAN B., ROSTAMY-MALKHALIFEH M.
Operations Research and Decisions
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2021
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vol. 31
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issue 1
41-59
EN
Data envelopment analysis (DEA) is a well-known method that based on inputs and outputs calculates the efficiency of decision-making units (DMUs). Comparing the efficiency and ranking of DMUs in different periods lets the decision-makers prevent any loss in the productivity of units and improve the production planning. Despite the merits of DEA models, they are not able to forecast the efficiency of future periods with known input/output records of the DMUs. With this end in view, this study aims at proposing a forecasting algorithm with a 95% confidence interval to generate fuzzy data sets for future periods. Moreover, managers’ opinions are inserted in the proposed forecasting model. Equipped with the forecasted data sets and concerning the data sets from earlier periods, this model can rightly forecast the efficiency of the future periods. The proposed procedure also employs the simple geometric mean to discriminate between efficient units. Examples from a real case including 20 automobile firms show the applicability of the proposed algorithm.
11
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Frequentist inference on traffic intensity of M/M/1 queuing system

61%
Dutta K., Choudhury A.
Operations Research and Decisions
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2023
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vol. 33
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issue 1
EN
When we study any queuing system, the performance measures reflect different features of the system. In the classical M/M/1 queuing system, traffic intensity is perhaps the most important performance measure. We propose a fresh and simple estimator for the same and show that it has nice properties. Our approach is frequentist. This approach has the dual advantage of practical usability and familiarity. Our proposed estimator is attractive as it possesses desirable properties. We have shown how our estimator lends itself to testing of hypothesis. Confidence intervals are constructed. Sample size determination is also discussed. A comparison with a few similar estimators is also performed.
12
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Interpretacja statystyk w ar tykułach naukowych – wskazówki dla praktyków

51%
Budzicz Ł.
Psychologiczne Zeszyty Naukowe
|
2017
|
issue 1
143-158
PL
Artykuł zawiera informacje o tym, jak interpretować podstawowe dane statystyczne: wskaźniki istotności statystycznej, wielkości efektu i przedziały ufności. Pokazano kilka heurystyk użytecznych przy interpretacji wielkości efektów korelacji r Pearsona, statystyki d Cohena oraz relatywnego ryzyka. Olbrzymia większość pozostałych efektów jest pochodną wyżej wymienionych. Dodatkowo wskazano również, jakie są ograniczenia wybranych wskaźników, szczególnie istotności statystycznej. Artykuł jest pomyślany jako pomoc szczególnie dla psychologów praktyków.
EN
The article contains information how to interpret statistical data: statistical significance, effect size and confidence intervals. Several heuristics are given how to usefully interpret the magnitude of the correlation Pearson’s r, Cohen’s d and relative risk. The vast majority of other effects is a derivative of the aforementioned. In addition, I also show the limitations of selected indicators, especially statistical significance. This article is intended as an aid especially for psychologists practitioners.
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