Asymptotics for the linear kernel quantile estimator

Test ◽  
2019 ◽  
Vol 28 (4) ◽  
pp. 1144-1174 ◽  
Author(s):  
Xuejun Wang ◽  
Yi Wu ◽  
Wei Yu ◽  
Wenzhi Yang ◽  
Shuhe Hu
Keyword(s):  
Linear Kernel ◽  
Kernel Quantile Estimator

scholarly journals Bahadur representation of linear kernel quantile estimator of VaR under assumptions

2010 ◽  
Vol 140 (7) ◽  
pp. 1620-1634 ◽  
Author(s):  
Xianglan Wei ◽  
Shanchao Yang ◽  
Keming Yu ◽  
Xin Yang ◽  
Guodong Xing
Keyword(s):  
Linear Kernel ◽  
Bahadur Representation ◽  
Kernel Quantile Estimator

Mackey Continuity of Characteristic Functionals

2002 ◽  
Vol 9 (1) ◽  
pp. 83-112
Author(s):  
S. Kwapień ◽  
V. Tarieladze
Keyword(s):  
Product Space ◽  
Locally Convex Space ◽  
Inner Product ◽  
Nuclear Space ◽  
Probability Measures ◽  
Linear Kernel ◽  
Characteristic Functional ◽  
Initial Space ◽  
Radon Probability Measure ◽  
Locally Convex

Abstract Problems of the Mackey-continuity of characteristic functionals and the localization of linear kernels of Radon probability measures in locally convex spaces are investigated. First the class of spaces is described, for which the continuity takes place. Then it is shown that in a non-complete sigmacompact inner product space, as well as in a non-complete sigma-compact metizable nuclear space, there may exist a Radon probability measure having a non-continuous characteristic functional in the Mackey topology and a linear kernel not contained in the initial space. Similar problems for moment forms and higher order kernels are also touched upon. Finally, a new proof of the result due to Chr. Borell is given, which asserts that any Gaussian Radon measure on an arbitrary Hausdorff locally convex space has the Mackey-continuous characteristic functional.

EFFECTS OF TEMPERATURE ON RATE AND DURATION OF KERNEL DRY MATTER ACCUMULATION OF MAIZE

1988 ◽  
Vol 68 (4) ◽  
pp. 935-940 ◽  
Author(s):  
M. TOLLENAAR ◽  
T. W. BRUULSEMA
Keyword(s):  
Growth Rate ◽  
Zea Mays ◽  
Dry Matter ◽  
Zea Mays L ◽  
Dry Weight ◽  
Controlled Environment ◽  
Dry Matter Accumulation ◽  
Linear Kernel ◽  
Effects Of Temperature ◽  
Kernel Growth

The response of rate and duration of kernel dry matter accumulation to temperatures in the range 10–25 °C was studied for two maize (Zea mays L.) hybrids grown under controlled-environment conditions. Kernel growth rates during the period of linear kernel growth increased linearly with temperature (b = 0.3 mg kernel−1 d−1 °C−1). Kernel dry weight at physiological maturity varied little among temperature treatments because the increase in kernel growth rate with increase in temperature was associated with a decline in the duration of kernel growth proportional to the increase in kernel growth rate.Key words: Zea mays L, period of linear kernel dry matter accumulation, controlled-environment conditions, kernel growth rate

The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

2020 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Komang Dhiyo Yonatha Wijaya ◽  
Anak Agung Istri Ngurah Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Kernel Method ◽  
Idle Time ◽  
Linear Kernel ◽  
Social Distancing ◽  
The Social ◽  
Negative Sentiment ◽  
F Measure ◽  
Kernel Yield

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

Sign in / Sign up

Export Citation Format

Share Document