Package index • lossratio

입력 계층

원시 experience 데이터의 검증·코어션 헬퍼.

check_experience(): Check an experience dataset
is_experience(): Check whether an object is an Experience
as_experience(): Coerce a dataset to an Experience object
add_experience_period(): Add standard period variables to an experience dataset
validate_triangle(): Validate triangle structure before building a development

같은 long-format experience 데이터를 보는 세 가지 프레임워크 — cohort × dev (Triangle), 달력 기간 (Calendar), 포트폴리오 전체 (Total).

build_triangle(): Build a development structure from experience data
build_calendar(): Build a calendar-based development structure from experience data
build_total(): Build a total development summary from experience data

chain ladder 방법의 기본 단위.

노출 기반 방법의 기본 단위.

chain ladder 와 손해율 추정. fit_lr 은 세 가지 method 지원 — "sa" (단계 적응형, default), "ed", "cl".

인수 코호트 간 구조적 변화 탐지.

detect_cohort_regime() print(<CohortRegime>) summary(<CohortRegime>) print(<summary.CohortRegime>): Detect structural regime shifts across underwriting cohorts

Triangle 의 최근 대각선을 보류한 뒤 재적합·예측을 보류된 실제값과 비교.

backtest() print(<Backtest>) summary(<Backtest>) print(<summary.Backtest>): Backtest a loss-ratio / chain ladder fit on existing data

예측 손해율이 갱신을 멈추고 수렴하는 발전 기간 ( $k^{**}$ ) 을 검출.

find_lr_convergence(): Find the development period at which the loss ratio estimate stabilises

plot() (base generic) 과 plot_triangle() (lossratio generic) 이 객체 클래스에 따라 dispatch.

plot_triangle(): Triangle plot generic
plot(<ATA>): Plot age-to-age factor diagnostics
plot(<ATAFit>): Plot an ata fit
plot(<Backtest>): Plot a backtest object
plot(<CLFit>): Plot a chain ladder fit
plot(<Calendar>): Plot calendar-based development statistics
plot(<CohortRegime>): Plot a cohort regime detection result
plot(<ED>): Plot ED intensity diagnostics
plot(<EDFit>): Plot an ED fit
plot(<LRConvergence>): Plot the LRConvergence diagnostic
plot(<LRFit>): Plot a loss ratio fit
plot(<Total>): Plot a Total object as a per-group bar chart
plot(<Triangle>): Plot development trajectories with optional summary overlay
plot_triangle(<ATA>): Plot ata factors as a triangle heatmap table
plot_triangle(<ATAFit>): Triangle heatmap for an ata fit
plot_triangle(<Backtest>): Triangle heatmap of backtest AEG
plot_triangle(<CLFit>): Plot chain ladder results as a triangle table
plot_triangle(<ED>): Plot ED intensities as a triangle heatmap table
plot_triangle(<EDFit>): Triangle heatmap for an ED fit
plot_triangle(<LRFit>): Plot loss ratio projection as a triangle heatmap
plot_triangle(<Triangle>): Plot development values as a triangle table

패키지 클래스에 등록된 print / summary / longer 메서드.

backtest() print(<Backtest>) summary(<Backtest>) print(<summary.Backtest>): Backtest a loss-ratio / chain ladder fit on existing data
detect_cohort_regime() print(<CohortRegime>) summary(<CohortRegime>) print(<summary.CohortRegime>): Detect structural regime shifts across underwriting cohorts
print(<ATAFit>): Print an ATAFit object
print(<CLFit>): Print a CLFit object
print(<EDFit>): Print an EDFit object
print(<LRFit>): Print an LRFit object
summary(<ATA>): Summarise age-to-age factor statistics
summary(<ATAFit>): Summary method for ATAFit
summary(<CLFit>): Summary method for CLFit
summary(<Calendar>): Summarise calendar-development statistics (Mean, Median, Weighted)
summary(<ED>): Summarise ED intensity statistics
summary(<EDFit>): Summary method for EDFit
summary(<LRFit>): Summary method for LRFit
summary(<Total>): Summarise a Total object
summary(<Triangle>): Summarise development statistics (Mean, Median, Weighted)