Table 12 Coefficients and MSEs of estimators in the number of equations and citation data.

From: Development of the generalized ridge estimator for the Poisson-Inverse Gaussian regression model with multicollinearity

Estimates

Parameters

\(\beta _0\)

\(\beta _1\)

\(\beta _2\)

\(\beta _3\)

\(\beta _4\)

\(\beta _5\)

\(\beta _6\)

MSE

PIGMLE

–

0.0683

0.027

0.1014

0.1054

− 0.0265

− 5e−04

− 0.0422

0.0237

PIGRRE

\(\hat{k}_1\)

0.0424

0.0215

0.101

0.1055

− 0.0198

0.0051

− 0.0365

0.0114

\(\hat{k}_2\)

0.0303

0.0179

0.1003

0.1055

− 0.0155

0.0089

− 0.0327

0.0078

\(\hat{k}_3\)

0.0451

0.0222

0.1011

0.1055

− 0.0206

0.0044

− 0.0372

0.0124

\(\hat{k}_4\)

0.0173

0.0128

0.0981

0.1056

− 0.0096

0.0144

− 0.0273

0.0058

\(\hat{k}_5\)

0.006

0.006

0.0171

0.0646

− 0.0036

0.0209

− 0.0102

0.0158

PIGGRE

\(\hat{K}_1\)

0.0012

0.0038

0.1155

0.0932

− 0.0012

0.0238

− 0.0167

0.0056

\(\hat{K}_2\)

0.0012

0.0037

0.1156

0.0931

− 0.0011

0.0239

− 0.0167

0.0056

\(\hat{K}_3\)

0.0012

0.0037

0.1157

0.093

− 0.001

0.024

− 0.0166

0.0055

\(\hat{K}_4\)

0.0005

0.002

0.1306

0.0566

− 0.0039

0.0294

− 0.0114

0.0068

\(\hat{K}_5\)

0.0069

0.0168

0.1014

0.1048

− 0.0135

0.01

− 0.0313

0.0066

  1. Bolded values indicate the best biasing parameter.