ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS

Debashis Mondal; Xiaohui Chang

doi:10.1214/24-AOAS1923

ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS

Debashis Mondal
, Xiaohui Chang

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Environmental bioassays, such as sediment toxicity tests, provide a broad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York–New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of specific contaminants. The proposed spatial analysis is based on non-Euclidean distances and is applicable to complex sampling domains with nonconvex boundaries. The findings suggest that current practices can be improved with the use of relevant statistical methods with odds, risk, survival probability ratios, and attributable effects.

Original language	English
Pages (from-to)	1086-1109
Number of pages	24
Journal	Annals of Applied Statistics
Volume	19
Issue number	2
DOIs	https://doi.org/10.1214/24-AOAS1923
State	Published - Jun 2025

Keywords

2 × 2 tables
REML
Sediment toxicity test
attributable effect
de Wijs process
h-likelihood
intrinsic autoregression
resistance distance
spatial bootstrap
survival probability ratio

Access to Document

10.1214/24-AOAS1923

Cite this

@article{72d76b718f974dfcb0da78f21c09b240,

title = "ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS",

abstract = "Environmental bioassays, such as sediment toxicity tests, provide a broad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York–New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of specific contaminants. The proposed spatial analysis is based on non-Euclidean distances and is applicable to complex sampling domains with nonconvex boundaries. The findings suggest that current practices can be improved with the use of relevant statistical methods with odds, risk, survival probability ratios, and attributable effects.",

keywords = "2 × 2 tables, REML, Sediment toxicity test, attributable effect, de Wijs process, h-likelihood, intrinsic autoregression, resistance distance, spatial bootstrap, survival probability ratio",

author = "Debashis Mondal and Xiaohui Chang",

note = "Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2025.",

year = "2025",

month = jun,

doi = "10.1214/24-AOAS1923",

language = "English",

volume = "19",

pages = "1086--1109",

journal = "Annals of Applied Statistics",

issn = "1932-6157",

number = "2",

}

TY - JOUR

T1 - ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS

AU - Mondal, Debashis

AU - Chang, Xiaohui

N1 - Publisher Copyright: © Institute of Mathematical Statistics, 2025.

PY - 2025/6

Y1 - 2025/6

N2 - Environmental bioassays, such as sediment toxicity tests, provide a broad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York–New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of specific contaminants. The proposed spatial analysis is based on non-Euclidean distances and is applicable to complex sampling domains with nonconvex boundaries. The findings suggest that current practices can be improved with the use of relevant statistical methods with odds, risk, survival probability ratios, and attributable effects.

AB - Environmental bioassays, such as sediment toxicity tests, provide a broad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York–New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of specific contaminants. The proposed spatial analysis is based on non-Euclidean distances and is applicable to complex sampling domains with nonconvex boundaries. The findings suggest that current practices can be improved with the use of relevant statistical methods with odds, risk, survival probability ratios, and attributable effects.

KW - 2 × 2 tables

KW - REML

KW - Sediment toxicity test

KW - attributable effect

KW - de Wijs process

KW - h-likelihood

KW - intrinsic autoregression

KW - resistance distance

KW - spatial bootstrap

KW - survival probability ratio

UR - https://www.scopus.com/pages/publications/105007987513

U2 - 10.1214/24-AOAS1923

DO - 10.1214/24-AOAS1923

M3 - Article

AN - SCOPUS:105007987513

SN - 1932-6157

VL - 19

SP - 1086

EP - 1109

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

IS - 2

ER -

ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this