Reducing the Statistical Distribution Error in Gridded Precipitation Data for the Tibetan Plateau

Gridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. A thin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP.

Study on the Wandering Weekday Effect in the Indonesian Capital Market Based on Trend Moderation Effect

This study investigates a wandering weekday effect, an assumption anomaly from fixed weekday effect to changes over time, under the moderation effect of market trend. We employ daily price data from the Jakarta Stock Exchange (JKSE) from 2000 to 2019. This study reveals that the fixed weekday effect has diminished when we introduced a market trend. Using robustness of distribution error, our further studies find that there is a negative wandering Monday effect when the market is falling. The findings provide a crucial contribution to market efficiency and help to reconcile mixed findings in previous studies

Aplikasi Regresi Linier Berganda Pada Hubungan Kepadatan Hunian Dan Perilaku Menguras Bak Mandi Dengan Kejadian Penyakit Malaria Di Indonesia

The purpose of this research is to modelize the relationship between density residential and the behavior of tub drain with the incidence of malaria in Indonesia. This research used multiple linear regression method with 1 dependent variable and 2 independent variables. The assumption multiple linier regression is an error followed a normal distribution, error variance is homoskedastisitas, no autocorrelation problem and there was no problem of multicollinearity between independent variables. The result showed that all assumptions of multiple linier regression were fulfilled. Overall test results by using F statistic was obtained value of p = 0,001 < 0,05 (alpha) it meant that at least one variable density residential or behavior of tub drain which had a significant effect on the incidence of malaria. The test results partly by using t statistics was obtained value of p variable density residential was 0,001 ; 0,05 which meant that density residential had effect on the incidence of malaria and value of p for the behavior of tub drain variable was 0,980 ; 0,05 which meant that the behavior tub drain had no effect on the incidence of malaria; Keywords : multiple linier regression, density residential, behavior, malaria