Robust Statistical Procedures: Second Edition

¡ SIAM
āχāĻŦ⧁āĻ•
77
āĻĒ⧃āĻˇā§āĻ āĻž
āϝ⧋āĻ—ā§āϝ
āĻŽā§‚āĻ˛ā§āϝāĻžāĻ‚āĻ•āύ āφ⧰⧁ āĻĒā§°ā§āϝāĻžāϞ⧋āϚāύāĻž āϏāĻ¤ā§āϝāĻžāĻĒāύ āϕ⧰āĻž āĻšā§‹ā§ąāĻž āύāĻžāχ  āĻ…āϧāĻŋāĻ• āϜāĻžāύāĻ•

āĻāχ āχāĻŦ⧁āĻ•āĻ–āύ⧰ āĻŦāĻŋāĻˇā§Ÿā§‡

Here is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition.

āĻāχ āχāĻŦ⧁āĻ•āĻ–āύāĻ• āĻŽā§‚āĻ˛ā§āϝāĻžāĻ‚āĻ•āύ āϕ⧰āĻ•

āφāĻŽāĻžāĻ• āφāĻĒā§‹āύāĻžā§° āĻŽāϤāĻžāĻŽāϤ āϜāύāĻžāĻ“āĻ•āĨ¤

āĻĒāĻĸāĻŧāĻžā§° āύāĻŋāĻ°ā§āĻĻ⧇āĻļāĻžā§ąāϞ⧀

āĻ¸ā§āĻŽāĻžā§°ā§āϟāĻĢ’āύ āφ⧰⧁ āĻŸā§‡āĻŦāϞ⧇āϟ
Android āφ⧰⧁ iPad/iPhoneā§° āĻŦāĻžāĻŦ⧇ Google Play Books āĻāĻĒāĻŸā§‹ āχāύāĻˇā§āϟāϞ āϕ⧰āĻ•āĨ¤ āχ āĻ¸ā§āĻŦāϝāĻŧāĻ‚āĻ•ā§āϰāĻŋāϝāĻŧāĻ­āĻžā§ąā§‡ āφāĻĒā§‹āύāĻžā§° āĻāĻ•āĻžāωāĻŖā§āϟ⧰ āϏ⧈āϤ⧇ āĻ›āĻŋāĻ‚āĻ• āĻšāϝāĻŧ āφ⧰⧁ āφāĻĒ⧁āύāĻŋ āϝ'āϤ⧇ āύāĻžāĻĨāĻžāĻ•āĻ• āϤ'āϤ⧇āχ āϕ⧋āύ⧋ āĻ…āĻĄāĻŋāĻ…'āĻŦ⧁āĻ• āĻ…āύāϞāĻžāχāύ āĻŦāĻž āĻ…āĻĢāϞāĻžāχāύāϤ āĻļ⧁āύāĻŋāĻŦāϞ⧈ āϏ⧁āĻŦāĻŋāϧāĻž āĻĻāĻŋāϝāĻŧ⧇āĨ¤
āϞ⧇āĻĒāϟāĻĒ āφ⧰⧁ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžā§°
āφāĻĒ⧁āύāĻŋ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžā§°ā§° ā§ąā§‡āĻŦ āĻŦā§āϰāĻžāωāϜāĻžā§° āĻŦā§āĻ¯ā§ąāĻšāĻžā§° āϕ⧰āĻŋ Google PlayāϤ āĻ•āĻŋāύāĻž āĻ…āĻĄāĻŋāĻ…'āĻŦ⧁āĻ•āϏāĻŽā§‚āĻš āĻļ⧁āύāĻŋāĻŦ āĻĒāĻžā§°ā§‡āĨ¤
āχ-ā§°ā§€āĻĄāĻžā§° āφ⧰⧁ āĻ…āĻ¨ā§āϝ āĻĄāĻŋāĻ­āĻžāχāϚ
Kobo eReadersā§° āĻĻ⧰⧇ āχ-āϚāĻŋ⧟āĻžāρāĻšā§€ā§° āĻĄāĻŋāĻ­āĻžāχāϚāϏāĻŽā§‚āĻšāϤ āĻĒā§āĻŋāĻŦāϞ⧈, āφāĻĒ⧁āύāĻŋ āĻāϟāĻž āĻĢāĻžāχāϞ āĻĄāĻžāωāύāĻ˛â€™āĻĄ āϕ⧰āĻŋ āϏ⧇āχāĻŸā§‹ āφāĻĒā§‹āύāĻžā§° āĻĄāĻŋāĻ­āĻžāχāϚāϞ⧈ āĻ¸ā§āĻĨāĻžāύāĻžāĻ¨ā§āϤ⧰āĻŖ āϕ⧰āĻŋāĻŦ āϞāĻžāĻ—āĻŋāĻŦāĨ¤ āϏāĻŽā§°ā§āĻĨāĻŋāϤ āχ-ā§°āĻŋāĻĄāĻžā§°āϞ⧈ āĻĢāĻžāχāϞāĻŸā§‹ āϕ⧇āύ⧇āĻ•ā§ˆ āĻ¸ā§āĻĨāĻžāύāĻžāĻ¨ā§āϤ⧰ āϕ⧰āĻŋāĻŦ āϜāĻžāύāĻŋāĻŦāϞ⧈ āϏāĻšāĻžāϝāĻŧ āϕ⧇āĻ¨ā§āĻĻā§ā§°āϤ āĻĨāĻ•āĻž āϏāĻŦāĻŋāĻļ⧇āώ āύāĻŋā§°ā§āĻĻ⧇āĻļāĻžā§ąāϞ⧀ āϚāĻžāĻ“āĻ•āĨ¤