3-phase faults & Unsym.įaults– Part I): (14 Hrs.) (Plus 3 concluding Sessions) MS Raviprakasha: Chapters # 1, 2 & 4 (Introduction, GK Purushothama, Professor of E&EE, MCE, Hassan MS Raviprakasha, Professor of E&EE, MCE, Hassan January 2002.Subject: POWER SYSTEM ANALYSIS AND STABILITY Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Hurvich Clifford M., Simonoff Jeffrey S., and Tsai Chih-Ling.
#SHOW Z1 U Z2 IS A NULL SET SERIES#
Regression and time series model selection in small samples. In Selected Papers of Hirotugu Akaike, Springer Series in Statistics, pages 199–213. Information Theory and an Extension of the Maximum Likelihood Principle. Springer- Verlag, New York, 2 edition, 2009.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition.
![show z1 u z2 is a null set show z1 u z2 is a null set](https://www.mathworks.com/help/examples/ident/win64/idnlbbdemo_siso_01.png)
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Xiang Zhan, Anna Plantinga, Ni Zhao, and Michael C.Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. Building on this code, user can continue to refine the visualization (e.g., by adding in confidence levels) and use it to improve the the model fit based on domain knowledge (e.g., by experimenting different kernels / hyper-parameters). From the figure we see that crime rate does impact the relationship between the local socioeconomic status v.s. Numbers at the end of each curves indicate the actual values of \(crim\) rate (per capita crime rate by town) at the corresponding quantiles. The figure above shows the \(medv\) - \(lstat\) relationship under different levels of \(crim\). # first fit the alternative model formula_alt Warning in ame(pred_cov_df, lstat = lstat_list, crim = #> crim_quantiles): row names were found from a short variable and have been #> discarded data_test1_pred Warning in ame(pred_cov_df, lstat = lstat_list, crim = #> crim_quantiles): row names were found from a short variable and have been #> discarded data_test2_pred Warning in ame(pred_cov_df, lstat = lstat_list, crim = #> crim_quantiles): row names were found from a short variable and have been #> discarded data_test3_pred Warning in ame(pred_cov_df, lstat = lstat_list, crim = #> crim_quantiles): row names were found from a short variable and have been #> discarded data_test4_pred Warning in ame(pred_cov_df, lstat = lstat_list, crim = #> crim_quantiles): row names were found from a short variable and have been #> discarded data_test5_pred <- predict(fit_bos_alt, data_test5) # combine five sets of prediction data together medv <- rbind(data_test1_pred, data_test2_pred, data_test3_pred, data_test4_pred, data_test5_pred) data_pred <- ame( lstat = rep(lstat_list, 5), medv = medv, crim = rep( c( "5% quantile", "25% quantile", "50% quantile", "75% quantile", "95% quantile"), each = 100)) data_pred $crim <- factor(data_pred $crim, levels = c( "5% quantile", "25% quantile", "50% quantile", "75% quantile", "95% quantile")) data_label <- data_pred data_label $value <- c( "0.028%", "0.082%", "0.257%", "3.677%", "15.789%") data_label $value <- factor(data_label $value, levels = c( "0.028%", "0.082%", "0.257%", "3.677%", "15.789%")) ggplot( data = data_pred, aes( x = lstat, y = medv, color = crim)) + geom_point( size = 0.1) + geom_text_repel( aes( label = value), data = data_label, color = "black", size = 3.6) + scale_colour_manual( values = c( "firebrick1", "chocolate2", "darkolivegreen3", "skyblue2", "purple2")) + geom_line() + theme_set( theme_bw()) + theme( id = element_blank(), = element_text( size = 12), = element_text( size = 12), legend.title = element_text( size = 12, face = "bold"), legend.text = element_text( size = 12)) + labs( x = "percentage of lower status", y = "median value of owner-occupied homes ($1000)", col = "per capita crime rate")
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001]) U0 <- Ks $u h1_prime_hat <- fitted( lm(h1_prime ~ U0)) h1 <- h1_prime - h1_prime_hat h1 <- h1 / sqrt( sum(h1 ^ 2)) # standardize interaction effect Y <- h0 + int_effect * h1 + rnorm( 1) + rnorm(n, 0, 0.01) data <- as.ame( cbind(Y, Z1, Z2)) colnames(data) <- c( "y", paste0( "z", 1 :d)) data_train <- data data_test <- data