gbanyan
da455791de
Address all 29 items from the fused reviewer report (Gemini 3.1 Pro + ChatGPT 5.5 + Opus 4.8): 3 fatal, 4 severe, arbitration A/B, 5 fusion-new, 15 minor. All new numbers computed from signature_analysis.db; nothing fabricated. Claim honesty (F1/F3/F4/F7/G3): - Retract all "139x the floor" comparisons; ICCR -> between-accountant specificity proxy throughout; state within-accountant FPR is not estimable and ICCR is not even a bound (anti-conservative direction). - Firm A reframed as quasi-positive known-positive benchmark (not blinded). - byte-identity recast as prevalence signal, not a recall/sanity check. - tunable -> single-direction conservativeness dial (no P-R frontier). New data analysis (verified, bit-reproducible via committed scripts): - F2/G1 (Sec V-B): 880-PDF imaging-pipeline audit (Table V) - plain scans 82% (2013) -> 1% (2021); producer strings name scanner hardware (Fuji Xerox D125 etc.); substrate transforms at 2020/21 = named confound. - F5 (Sec IV-C): four robustness checks - pool-size stratification, accountant-clustered bootstrap (gap 53.7pp [49.5,57.5]), firm+year FE logistic (B/C/D OR 0.06-0.12), leave-one-year-out (gap 53.1-54.9pp). - byte-identity era split: 30 scan-era (18 Firm A, pipeline-robust) vs 232 digital-era (detectability-inflated, hedged). - G5: archive-wide 888 expected chance HC flags [677,1098]. - M4: Figure 3 replaced with real 2D density (n=150,441). Structure/minor: abstract restructured (M1); operational definition (M2); interview disclaimer (M3); Threats to Validity subsection (M8); review protocol framed as design not evidence (M9); N reconciliations (M10/M11); Table II-c 2020-23 five-way (M12); Section refs, American spelling, notation table (M5/M13/M15); reference URLs verified (M14). Open (author-only): placeholders (M13), II-b/IV table merge (M15). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qn59FdF9JMyfFg3sjcUNNG
64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
"""F5 robustness: firm+year fixed-effects logistic regression and leave-one-year-out.
|
|
Complements the pool-size stratification and accountant-clustered bootstrap (Section IV-C).
|
|
Uses numpy+scipy only (no statsmodels). Reproduces from signature_analysis.db.
|
|
"""
|
|
import sqlite3, numpy as np
|
|
from scipy.optimize import minimize
|
|
|
|
DB = "/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db"
|
|
BIG4 = ('勤業眾信聯合', '資誠聯合', '安侯建業聯合', '安永聯合')
|
|
FM = {'勤業眾信聯合': 'A', '安侯建業聯合': 'B', '資誠聯合': 'C', '安永聯合': 'D'}
|
|
|
|
con = sqlite3.connect(DB); cur = con.cursor()
|
|
cur.execute(f"""
|
|
SELECT s.excel_firm, CAST(substr(s.year_month,1,4) AS INT) yr,
|
|
(CASE WHEN s.max_similarity_to_same_accountant>0.95 AND s.min_dhash_independent<=5 THEN 1 ELSE 0 END) hc,
|
|
p.psize
|
|
FROM signatures s
|
|
JOIN (SELECT accountant_id, COUNT(*) psize FROM signatures
|
|
WHERE is_valid=1 AND excel_firm IN ({','.join('?'*4)})
|
|
AND max_similarity_to_same_accountant IS NOT NULL AND min_dhash_independent IS NOT NULL
|
|
GROUP BY accountant_id) p ON s.accountant_id=p.accountant_id
|
|
WHERE s.is_valid=1 AND s.excel_firm IN ({','.join('?'*4)})
|
|
AND s.max_similarity_to_same_accountant IS NOT NULL AND s.min_dhash_independent IS NOT NULL
|
|
AND s.year_month GLOB '2[0-9][0-9][0-9][0-9][0-9]'
|
|
""", BIG4 + BIG4)
|
|
rows = cur.fetchall(); con.close()
|
|
firm = np.array([FM[r[0]] for r in rows]); yr = np.array([r[1] for r in rows])
|
|
hc = np.array([r[2] for r in rows], float); pool = np.array([r[3] for r in rows], float)
|
|
n = len(hc); years = sorted(set(yr.tolist()))
|
|
|
|
# --- firm + year FE logistic (Firm A & first year = reference) ---
|
|
cols = [np.ones(n)]; names = ['const']
|
|
for f in ['B', 'C', 'D']:
|
|
cols.append((firm == f).astype(float)); names.append(f'firm_{f}')
|
|
for y in years[1:]:
|
|
cols.append((yr == y).astype(float)); names.append(f'yr_{y}')
|
|
lp = np.log(pool); lp = (lp - lp.mean()) / lp.std()
|
|
cols.append(lp); names.append('logpool_z')
|
|
X = np.column_stack(cols)
|
|
|
|
def nll(b):
|
|
z = X @ b
|
|
return -np.sum(hc * z - np.logaddexp(0, z)) + 1e-6 * np.sum(b * b)
|
|
def grad(b):
|
|
p = 1 / (1 + np.exp(-(X @ b)))
|
|
return -X.T @ (hc - p) + 2e-6 * b
|
|
b = minimize(nll, np.zeros(X.shape[1]), jac=grad, method='L-BFGS-B').x
|
|
print("Firm+Year FE logistic (Firm A & first year = ref):")
|
|
for nm, bi in zip(names, b):
|
|
if nm.startswith('firm') or nm == 'logpool_z':
|
|
print(f" {nm:11} coef={bi:7.3f} OR={np.exp(bi):.4f}")
|
|
|
|
# --- leave-one-year-out firm contrast ---
|
|
grp = np.where(firm == 'A', 'A', 'BCD')
|
|
def rate(mask, g):
|
|
m = mask & (grp == g); return 100 * hc[m].mean()
|
|
print("\nLeave-one-year-out (Firm A minus B/C/D HC gap):")
|
|
gaps = []
|
|
for y in years:
|
|
keep = (yr != y); a = rate(keep, 'A'); bb = rate(keep, 'BCD'); gaps.append(a - bb)
|
|
print(f" drop {y}: A={a:.1f}% BCD={bb:.1f}% gap={a-bb:.1f}pp")
|
|
print(f" full-sample gap={rate(np.ones(n, bool),'A')-rate(np.ones(n, bool),'BCD'):.1f}pp; "
|
|
f"LOYO range=[{min(gaps):.1f}, {max(gaps):.1f}]pp")
|