Improved estimation of site occupancy using penalized likelihood.
نویسندگان
چکیده
When detection or occupancy probability is small or when the number of sites and number of visits per site is small, maximum likelihood estimators (MLE) of site occupancy parameters have large biases, are numerically unstable, and the corresponding confidence intervals have smaller than nominal coverage. We propose an alternative method of estimation, based on penalized likelihood. This method is numerically stable, the estimators have smaller mean square error than the MLE, and associated confidence intervals have close to nominal coverage.
منابع مشابه
Penalized Bregman Divergence Estimation via Coordinate Descent
Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...
متن کاملDealing with detection error in site occupancy surveys: what can we do with a single survey?
Aim Site occupancy probabilities of target species are commonly used in various ecological studies, e.g. to monitor current status and trends in biodiversity. Detection error introduces bias in the estimators of site occupancy. Existing methods for estimating occupancy probability in the presence of detection error use replicate surveys. These methods assume population closure, i.e. the site oc...
متن کاملIMPROVING GAUSSIAN MIXTURE DENSITY ESTIMATES 1 Averaging
We apply the idea of averaging ensembles of estimators to probability density estimation. In particular we use Gaussian mixture models which are important components in many neural network applications. One variant of averaging is Breiman's \bagging", which recently produced impressive results in classiication tasks. We investigate the performance of averaging using three data sets. For compari...
متن کاملA New Approach to Self-Localization for Mobile Robots Using Sensor Data Fusion
This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such ...
متن کاملAPPLE: Approximate Path for Penalized Likelihood Estimators
In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both convex penalties (such as LASSO) and folded concave penalties (such as MCP) are considered. APPLE efficiently computes the s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Ecology
دوره 91 2 شماره
صفحات -
تاریخ انتشار 2010