語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Complexity Penalized Methods for Str...
~
Goeva, Aleksandrina Valerieva.
FindBook
Google Book
Amazon
博客來
Complexity Penalized Methods for Structured and Unstructured Data.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Complexity Penalized Methods for Structured and Unstructured Data./
作者:
Goeva, Aleksandrina Valerieva.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
136 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:
Dissertation Abstracts International79-04B(E).
標題:
Statistics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268226
ISBN:
9780355460612
Complexity Penalized Methods for Structured and Unstructured Data.
Goeva, Aleksandrina Valerieva.
Complexity Penalized Methods for Structured and Unstructured Data.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 136 p.
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)--Boston University, 2017.
A fundamental goal of statisticians is to make inferences from the sample about characteristics of the underlying population. This is an inverse problem, since we are trying to recover a feature of the input with the availability of observations on an output. Towards this end, we consider complexity penalized methods, because they balance goodness of fit and generalizability of the solution. The data from the underlying population may come in diverse formats - structured or unstructured - such as probability distributions, text tokens, or graph characteristics. Depending on the defining features of the problem we can chose the appropriate complexity penalized approach, and assess the quality of the estimate produced by it. Favorable characteristics are strong theoretical guarantees of closeness to the true value and interpretability. Our work fits within this framework and spans the areas of simulation optimization, text mining and network inference. The first problem we consider is model calibration under the assumption that given a hypothesized input model, we can use stochastic simulation to obtain its corresponding output observations. We formulate it as a stochastic program by maximizing the entropy of the input distribution subject to moment matching. We then propose an iterative scheme via simulation to approximately solve it. We prove convergence of the proposed algorithm under appropriate conditions and demonstrate the performance via numerical studies. The second problem we consider is summarizing text documents through an inferred set of topics. We propose a frequentist reformulation of a Bayesian regularization scheme. Through our complexity-penalized perspective we lend further insight into the nature of the loss function and the regularization achieved through the priors in the Bayesian formulation. The third problem is concerned with the impact of sampling on the degree distribution of a network. Under many sampling designs, we have a linear inverse problem characterized by an ill-conditioned matrix. We investigate the theoretical properties of an approximate solution for the degree distribution found by regularizing the solution of the ill-conditioned least squares objective. Particularly, we study the rate at which the penalized solution tends to the true value as a function of network size and sampling rate.
ISBN: 9780355460612Subjects--Topical Terms:
517247
Statistics.
Complexity Penalized Methods for Structured and Unstructured Data.
LDR
:03329nmm a2200313 4500
001
2166101
005
20181203094030.5
008
190424s2017 ||||||||||||||||| ||eng d
020
$a
9780355460612
035
$a
(MiAaPQ)AAI10268226
035
$a
(MiAaPQ)bu:12954
035
$a
AAI10268226
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Goeva, Aleksandrina Valerieva.
$3
3354206
245
1 0
$a
Complexity Penalized Methods for Structured and Unstructured Data.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2017
300
$a
136 p.
500
$a
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500
$a
Advisers: Eric D. Kolaczyk; Henry Lam.
502
$a
Thesis (Ph.D.)--Boston University, 2017.
520
$a
A fundamental goal of statisticians is to make inferences from the sample about characteristics of the underlying population. This is an inverse problem, since we are trying to recover a feature of the input with the availability of observations on an output. Towards this end, we consider complexity penalized methods, because they balance goodness of fit and generalizability of the solution. The data from the underlying population may come in diverse formats - structured or unstructured - such as probability distributions, text tokens, or graph characteristics. Depending on the defining features of the problem we can chose the appropriate complexity penalized approach, and assess the quality of the estimate produced by it. Favorable characteristics are strong theoretical guarantees of closeness to the true value and interpretability. Our work fits within this framework and spans the areas of simulation optimization, text mining and network inference. The first problem we consider is model calibration under the assumption that given a hypothesized input model, we can use stochastic simulation to obtain its corresponding output observations. We formulate it as a stochastic program by maximizing the entropy of the input distribution subject to moment matching. We then propose an iterative scheme via simulation to approximately solve it. We prove convergence of the proposed algorithm under appropriate conditions and demonstrate the performance via numerical studies. The second problem we consider is summarizing text documents through an inferred set of topics. We propose a frequentist reformulation of a Bayesian regularization scheme. Through our complexity-penalized perspective we lend further insight into the nature of the loss function and the regularization achieved through the priors in the Bayesian formulation. The third problem is concerned with the impact of sampling on the degree distribution of a network. Under many sampling designs, we have a linear inverse problem characterized by an ill-conditioned matrix. We investigate the theoretical properties of an approximate solution for the degree distribution found by regularizing the solution of the ill-conditioned least squares objective. Particularly, we study the rate at which the penalized solution tends to the true value as a function of network size and sampling rate.
590
$a
School code: 0017.
650
4
$a
Statistics.
$3
517247
650
4
$a
Mathematics.
$3
515831
650
4
$a
Applied mathematics.
$3
2122814
690
$a
0463
690
$a
0405
690
$a
0364
710
2
$a
Boston University.
$b
Mathematics and Statistics.
$3
2122816
773
0
$t
Dissertation Abstracts International
$g
79-04B(E).
790
$a
0017
791
$a
Ph.D.
792
$a
2017
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268226
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9365648
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入